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Maple (software) : The Maple engine is used within several other products from Maplesoft: MapleNet allows users to create JSP pages and Java Applets. MapleNet 12 and above also allow users to upload and work with Maple worksheets containing interactive components. MapleSim, an engineering simulation tool. Maple Quantum Chemistry Package from RDMChem computes and visualizes the electronic energies and properties of molecules. Listed below are third-party commercial products that no longer use the Maple engine: Versions of Mathcad released between 1994 and 2006 included a Maple-derived algebra engine (MKM, aka Mathsoft Kernel Maple), though subsequent versions use MuPAD. Symbolic Math Toolbox in MATLAB contained a portion of the Maple 10 engine, but now uses MuPAD (starting with MATLAB R2007b+ release). Older versions of the mathematical editor Scientific Workplace included Maple as a computational engine, though current versions include MuPAD.
Maple (software) : Maplesoft, division of Waterloo Maple, Inc. – official website
Massive Online Analysis : Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.
Massive Online Analysis : MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API. MOA contains several collections of machine learning algorithms: Classification Bayesian classifiers Naive Bayes Naive Bayes Multinomial Decision trees classifiers Decision Stump Hoeffding Tree Hoeffding Option Tree Hoeffding Adaptive Tree Meta classifiers Bagging Boosting Bagging using ADWIN Bagging using Adaptive-Size Hoeffding Trees. Perceptron Stacking of Restricted Hoeffding Trees Leveraging Bagging Online Accuracy Updated Ensemble Function classifiers Perceptron Stochastic gradient descent (SGD) Pegasos Drift classifiers Self-Adjusting Memory Probabilistic Adaptive Windowing Multi-label classifiers Active learning classifiers Regression FIMTDD AMRules Clustering StreamKM++ CluStream ClusTree D-Stream CobWeb. Outlier detection STORM Abstract-C COD MCOD AnyOut Recommender systems BRISMFPredictor Frequent pattern mining Itemsets Graphs Change detection algorithms These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time. MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.
Massive Online Analysis : ADAMS Workflow: Workflow engine for MOA and Weka Streams: Flexible module environment for the design and execution of data stream experiments Vowpal Wabbit List of numerical analysis software
Massive Online Analysis : MOA Project home page at University of Waikato in New Zealand SAMOA Project home page at Yahoo Labs
MATLAB : MATLAB (an abbreviation of "MATrix LABoratory") is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. Although MATLAB is intended primarily for numeric computing, an optional toolbox uses the MuPAD symbolic engine allowing access to symbolic computing abilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems. As of 2020, MATLAB has more than four million users worldwide. They come from various backgrounds of engineering, science, and economics. As of 2017, more than 5000 global colleges and universities use MATLAB to support instruction and research.
MATLAB : For a complete list of changes of both MATLAB an official toolboxes, check MATLAB previous releases.
MATLAB : The MATLAB application is built around the MATLAB programming language. Common usage of the MATLAB application involves using the "Command Window" as an interactive mathematical shell or executing text files containing MATLAB code.
MATLAB : MATLAB has tightly integrated graph-plotting features. For example, the function plot can be used to produce a graph from two vectors x and y. The code: produces the following figure of the sine function: MATLAB supports three-dimensional graphics as well: MATLAB supports developing graphical user interface (GUI) applications. UIs can be generated either programmatically or using visual design environments such as GUIDE and App Designer.
MATLAB : MATLAB can call functions and subroutines written in the programming languages C or Fortran. A wrapper function is created allowing MATLAB data types to be passed and returned. MEX files (MATLAB executables) are the dynamically loadable object files created by compiling such functions. Since 2014 increasing two-way interfacing with Python was being added. Libraries written in Perl, Java, ActiveX or .NET can be directly called from MATLAB, and many MATLAB libraries (for example XML or SQL support) are implemented as wrappers around Java or ActiveX libraries. Calling MATLAB from Java is more complicated, but can be done with a MATLAB toolbox which is sold separately by MathWorks, or using an undocumented mechanism called JMI (Java-to-MATLAB Interface), (which should not be confused with the unrelated Java Metadata Interface that is also called JMI). Official MATLAB API for Java was added in 2016. As alternatives to the MuPAD based Symbolic Math Toolbox available from MathWorks, MATLAB can be connected to Maple or Mathematica. Libraries also exist to import and export MathML.
MATLAB : In 2020, MATLAB withdrew services from two Chinese universities as a result of US sanctions. The universities said this will be responded to by increased use of open-source alternatives and by developing domestic alternatives.
MATLAB : Comparison of numerical-analysis software List of numerical-analysis software
MATLAB : Official website
MeeMix : MeeMix Ltd is a company specializing in personalizing media-related content recommendations, discovery and advertising for the telecommunication industry, founded in 2006. On January 1, 2008, MeeMix launched meemix.com, a public personalized internet radio serving as an online testbed for the development of music taste-prediction technologies. Subsequently, MeeMix released in 2009 a line of Business-to-business commercial services intended to personalize media recommendations, discovery and advertising. MeeMix hybrid taste-prediction technology relies on integrating machine learning algorithms, digital signal processing, behavior analysis, metadata analysis and collaborative filtering, and is provided via API web service. In August 2009, MeeMix was announced as Innovator Nominee in the GSM Association’s Mobile Innovation Grand Prix worldwide contest. As of 2013, MeeMix no longer features internet radios on meemix.com. On Sep 28, 2014, meemix.com went offline.
MeeMix : Behavioral targeting Collaborative filtering Internet radio Personalization Predictive analytics
MeeMix : MeeMix online radio
Megvii : Megvii (Chinese: 旷视; pinyin: Kuàngshì) is a Chinese technology company that designs image recognition and deep-learning software. Based in Beijing, the company develops artificial intelligence (AI) technology for businesses and for the public sector. Megvii is the largest provider of third-party authentication software in the world, and its product, Face++, is the world's largest computer vision platform. In 2019, the company was valued at $USD 4 billion. As of 2024, the company operates the world's largest computer vision research institute. The company has faced U.S. investment and export restrictions due to allegations of aiding the persecution of Uyghurs in China.
Megvii : The company was founded in Beijing with Megvii standing for "mega vision." It was started by Yin Qi and two college friends.: 101 The company's core product, Face++, launched in 2012 as the first online facial recognition platform in China. In 2015 Megvii created Brain++, a deep-learning engine to help train its algorithms. Backed by GGV Capital, Megvii raised $100 million in 2016, $460 million in 2017 and $750 million in May 2019. In 2017, Megvii marketed authentication and computational photography functions to smart phone companies and mobile application developers, then smart logistics. Megvii's AI-empowered products include personal IoT, city IoT and supply chain IoT. In 2017 and 2018, Megvii beat Google, Facebook, and Microsoft in tests of image recognition at the International Conference on Computer Vision. By June 2019, Megvii had 2,349 employees, and was valued at over $4 billion, as the "world’s biggest provider of third-party authentication software", with 339 corporate clients in 112 cities in China. The Chinese government employs Megvii software. In May 2019, Human Rights Watch reported finding Face++ code in the Integrated Joint Operations Platform (IJOP), a police surveillance app used to collect data on, and track the Uyghur community in Xinjiang. Human Rights Watch released a correction to its report in June 2019 stating that Megvii did not appear to have collaborated on IJOP, and that the Face++ code in the app was inoperable. In March 2020, Megvii announced that it would make its deep learning framework MegEngine open-source. As of 2024, Megvii operates the world's largest computer vision research institute.: 101
Megvii : Official website
Mlpack : mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers. It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world.
Mlpack : mlpack includes a range of design features that make it particularly well-suited for specialized applications, especially in the Edge AI and IoT domains. Its C++ codebase allows for seamless integration with sensors, facilitating direct data extraction and on-device preprocessing at the Edge. Below, we outline a specific set of design features that highlight mlpack's capabilities in these environments:
Mlpack : The following shows a simple example how to train a decision tree model using mlpack, and to use it for the classification. Of course you can ingest your own dataset using the Load function, but for now we are showing the API:The above example demonstrate the simplicity behind the API design, which makes it similar to popular Python based machine learning kit (scikit-learn). Our objective is to simplify for the user the API and the main machine learning functions such as Classify and Predict. More complex examples are located in the examples repository, including documentations for the methods
Mlpack : mlpack is fiscally sponsored and supported by NumFOCUS, Consider making a tax-deductible donation to help the developers of the project. In addition mlpack team participates each year Google Summer of Code program and mentors several students.
Mlpack : Armadillo (C++ library) List of numerical analysis software List of numerical libraries Numerical linear algebra Scientific computing
Mlpack : Official website mlpack on GitHub
Mlpy : mlpy is a Python, open-source, machine learning library built on top of NumPy/SciPy, the GNU Scientific Library and it makes an extensive use of the Cython language. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3. Suited for general-purpose machine learning tasks, mlpy's motivating application field is bioinformatics, i.e. the analysis of high throughput omics data.
Mlpy : Regression: least squares, ridge regression, least angle regression, elastic net, kernel ridge regression, support vector machines (SVM), partial least squares (PLS) Classification: linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier Clustering: hierarchical clustering, Memory-saving Hierarchical Clustering, k-means Dimensionality reduction: (Kernel) Fisher discriminant analysis (FDA), Spectral Regression Discriminant Analysis (SRDA), (kernel) Principal component analysis (PCA) Kernel-based functions are managed through a common kernel layer. In particular, the user can choose between supplying the data or a precomputed kernel in input space. Linear, polynomial, Gaussian, exponential and sigmoid kernels are available as default choices, and custom kernels can be defined as well. Many classification and regression algorithms are endowed with an internal feature ranking procedure: in alternative, mlpy implements the I-Relief algorithm. Recursive feature elimination (RFE) for linear classifiers and the KFDA-RFE algorithm are available for feature selection. Methods for feature list analysis (for example the Canberra stability indicator), data resampling and error evaluation are provided, together with different clustering analysis methods (Hierarchical, Memory-saving Hierarchical, k-means). Finally, dedicated submodules are included for longitudinal data analysis through wavelet transform (Continuous, Discrete and Undecimated) and dynamic programming algorithms (Dynamic Time Warping and variants).
Mlpy : scikit-learn, an open source machine learning library for the Python programming language Infer.NET, an open source machine learning library for the .NET Framework
Mlpy : Official website
Oracle Data Mining : Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment.
Oracle Data Mining : Oracle Corporation has implemented a variety of data mining algorithms inside its Oracle Database relational database product. These implementations integrate directly with the Oracle database kernel and operate natively on data stored in the relational database tables. This eliminates the need for extraction or transfer of data into standalone mining/analytic servers. The relational database platform is leveraged to securely manage models and to efficiently execute SQL queries on large volumes of data. The system is organized around a few generic operations providing a general unified interface for data-mining functions. These operations include functions to create, apply, test, and manipulate data-mining models. Models are created and stored as database objects, and their management is done within the database - similar to tables, views, indexes and other database objects. In data mining, the process of using a model to derive predictions or descriptions of behavior that is yet to occur is called "scoring". In traditional analytic workbenches, a model built in the analytic engine has to be deployed in a mission-critical system to score new data, or the data is moved from relational tables into the analytical workbench - most workbenches offer proprietary scoring interfaces. ODM simplifies model deployment by offering Oracle SQL functions to score data stored right in the database. This way, the user/application-developer can leverage the full power of Oracle SQL - in terms of the ability to pipeline and manipulate the results over several levels, and in terms of parallelizing and partitioning data access for performance. Models can be created and managed by one of several means. Oracle Data Miner provides a graphical user interface that steps the user through the process of creating, testing, and applying models (e.g. along the lines of the CRISP-DM methodology). Application- and tools-developers can embed predictive and descriptive mining capabilities using PL/SQL or Java APIs. Business analysts can quickly experiment with, or demonstrate the power of, predictive analytics using Oracle Spreadsheet Add-In for Predictive Analytics, a dedicated Microsoft Excel adaptor interface. ODM offers a choice of well-known machine learning approaches such as Decision Trees, Naive Bayes, Support vector machines, Generalized linear model (GLM) for predictive mining, Association rules, K-means and Orthogonal Partitioning Clustering, and Non-negative matrix factorization for descriptive mining. A minimum description length based technique to grade the relative importance of input mining attributes for a given problem is also provided. Most Oracle Data Mining functions also allow text mining by accepting text (unstructured data) attributes as input. Users do not need to configure text-mining options - the Database_options database option handles this behind the scenes.
Oracle Data Mining : Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release: Oracle Data Mining 9iR2 (9.2.0.1.0 - May 2002) Oracle Data Mining 10gR1 (10.1.0.2.0 - February 2004) Oracle Data Mining 10gR2 (10.2.0.1.0 - July 2005) Oracle Data Mining 11gR1 (11.1 - September 2007) Oracle Data Mining 11gR2 (11.2 - September 2009) Oracle Data Mining is a logical successor of the Darwin data mining toolset developed by Thinking Machines Corporation in the mid-1990s and later distributed by Oracle after its acquisition of Thinking Machines in 1999. However, the product itself is a complete redesign and rewrite from ground-up - while Darwin was a classic GUI-based analytical workbench, ODM offers a data mining development/deployment platform integrated into the Oracle database, along with the Oracle Data Miner GUI. The Oracle Data Miner 11gR2 New Workflow GUI was previewed at Oracle Open World 2009. An updated Oracle Data Miner GUI was released in 2012. It is free, and is available as an extension to Oracle SQL Developer 3.1 .
Oracle Data Mining : As of release 11gR1 Oracle Data Mining contains the following data mining functions: Data transformation and model analysis: Data sampling, binning, discretization, and other data transformations. Model exploration, evaluation and analysis. Feature selection (Attribute Importance). Minimum description length (MDL). Classification. Naive Bayes (NB). Generalized linear model (GLM) for Logistic regression. Support Vector Machine (SVM). Decision Trees (DT). Anomaly detection. One-class Support Vector Machine (SVM). Regression Support Vector Machine (SVM). Generalized linear model (GLM) for Multiple regression Clustering: Enhanced k-means (EKM). Orthogonal Partitioning Clustering (O-Cluster). Association rule learning: Itemsets and association rules (AM). Feature extraction. Non-negative matrix factorization (NMF). Text and spatial mining: Combined text and non-text columns of input data. Spatial/GIS data.
Oracle Data Mining : Most Oracle Data Mining functions accept as input one relational table or view. Flat data can be combined with transactional data through the use of nested columns, enabling mining of data involving one-to-many relationships (e.g. a star schema). The full functionality of SQL can be used when preparing data for data mining, including dates and spatial data. Oracle Data Mining distinguishes numerical, categorical, and unstructured (text) attributes. The product also provides utilities for data preparation steps prior to model building such as outlier treatment, discretization, normalization and binning (sorting in general speak)
Oracle Data Mining : Users can access Oracle Data Mining through Oracle Data Miner, a GUI client application that provides access to the data mining functions and structured templates (called Mining Activities) that automatically prescribe the order of operations, perform required data transformations, and set model parameters. The user interface also allows the automated generation of Java and/or SQL code associated with the data-mining activities. The Java Code Generator is an extension to Oracle JDeveloper. An independent interface also exists: the Spreadsheet Add-In for Predictive Analytics which enables access to the Oracle Data Mining Predictive Analytics PL/SQL package from Microsoft Excel. From version 11.2 of the Oracle database, Oracle Data Miner integrates with Oracle SQL Developer.
Oracle Data Mining : Oracle Data Mining provides a native PL/SQL package (DBMS_DATA_MINING) to create, destroy, describe, apply, test, export and import models. The code below illustrates a typical call to build a classification model: where 'credit_risk_model' is the model name, built for the express purpose of classifying future customers' 'credit_risk', based on training data provided in the table 'credit_card_data', each case distinguished by a unique 'customer_id', with the rest of the model parameters specified through the table 'credit_risk_model_settings'. Oracle Data Mining also supports a Java API consistent with the Java Data Mining (JDM) standard for data mining (JSR-73) for enabling integration with web and Java EE applications and to facilitate portability across platforms.
Oracle Data Mining : As of release 10gR2, Oracle Data Mining contains built-in SQL functions for scoring data mining models. These single-row functions support classification, regression, anomaly detection, clustering, and feature extraction. The code below illustrates a typical usage of a classification model:
Oracle Data Mining : In Release 11gR2 (11.2.0.2), ODM supports the import of externally created PMML for some of the data mining models. PMML is an XML-based standard for representing data mining models.
Oracle Data Mining : The PL/SQL package DBMS_PREDICTIVE_ANALYTICS automates the data mining process including data preprocessing, model building and evaluation, and scoring of new data. The PREDICT operation is used for predicting target values classification or regression while EXPLAIN ranks attributes in order of influence in explaining a target column feature selection. The new 11g feature PROFILE finds customer segments and their profiles, given a target attribute. These operations can be used as part of an operational pipeline providing actionable results or displayed for interpretation by end users.
Oracle Data Mining : T. H. Davenport, Competing on Analytics, Harvard Business Review, January 2006. I. Ben-Gal,Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2. M. M. Campos, P. J. Stengard, and B. L. Milenova, Data-centric Automated Data Mining. In proceedings of the Fourth International Conference on Machine Learning and Applications 2005, 15–17 December 2005. pp8, ISBN 0-7695-2495-8 M. F. Hornick, Erik Marcade, and Sunil Venkayala. Java Data Mining: Strategy, Standard, and Practice. Morgan-Kaufmann, 2006, ISBN 0-12-370452-9. B. L. Milenova, J. S. Yarmus, and M. M. Campos. SVM in Oracle database 10g: removing the barriers to widespread adoption of support vector machines. In Proceedings of the 31st international Conference on Very Large Data Bases (Trondheim, Norway, August 30 - September 2, 2005). pp1152–1163, ISBN 1-59593-154-6. B. L. Milenova and M. M. Campos. O-Cluster: scalable clustering of large high dimensional data sets. In proceedings of the 2002 IEEE International Conference on Data Mining: ICDM 2002. pp290–297, ISBN 0-7695-1754-4. P. Tamayo, C. Berger, M. M. Campos, J. S. Yarmus, B. L.Milenova, A. Mozes, M. Taft, M. Hornick, R. Krishnan, S.Thomas, M. Kelly, D. Mukhin, R. Haberstroh, S. Stephens and J. Myczkowski. Oracle Data Mining - Data Mining in the Database Environment. In Part VII of Data Mining and Knowledge Discovery Handbook, Maimon, O.; Rokach, L. (Eds.) 2005, p315-1329, ISBN 0-387-24435-2. Brendan Tierney, Predictive Analytics using Oracle Data Miner: for the data scientist, oracle analyst, oracle developer & DBA, Oracle Press, McGraw Hill, Spring 2014.
Oracle Data Mining : Oracle LogMiner - in contrast to generic data mining, targets the extraction of information from the internal logs of an Oracle database
Oracle Data Mining : Oracle Data Mining at Oracle Technology Network. Oracle Data Mining Blog. Oracle Database 11g at Oracle Technology Network. Oracle Data Mining and Analytics Blog. Oracle Wiki for Data Mining. Oracle Data Mining RSS Feed. Oracle Data Mining at Oracle Technology Network. Oracle Data Mining related blog by Brendan Tierney (Oracle ACE Director). Oracle Data Mining Examples (on Panoply Technology).
Programming with Big Data in R : Programming with Big Data in R (pbdR) is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. The pbdR uses the same programming language as R with S3/S4 classes and methods which is used among statisticians and data miners for developing statistical software. The significant difference between pbdR and R code is that pbdR mainly focuses on distributed memory systems, where data are distributed across several processors and analyzed in a batch mode, while communications between processors are based on MPI that is easily used in large high-performance computing (HPC) systems. R system mainly focuses on single multi-core machines for data analysis via an interactive mode such as GUI interface. Two main implementations in R using MPI are Rmpi and pbdMPI of pbdR. The pbdR built on pbdMPI uses SPMD parallelism where every processor is considered as worker and owns parts of data. The SPMD parallelism introduced in mid 1980 is particularly efficient in homogeneous computing environments for large data, for example, performing singular value decomposition on a large matrix, or performing clustering analysis on high-dimensional large data. On the other hand, there is no restriction to use manager/workers parallelism in SPMD parallelism environment. The Rmpi uses manager/workers parallelism where one main processor (manager) serves as the control of all other processors (workers). The manager/workers parallelism introduced around early 2000 is particularly efficient for large tasks in small clusters, for example, bootstrap method and Monte Carlo simulation in applied statistics since i.i.d. assumption is commonly used in most statistical analysis. In particular, task pull parallelism has better performance for Rmpi in heterogeneous computing environments. The idea of SPMD parallelism is to let every processor do the same amount of work, but on different parts of a large data set. For example, a modern GPU is a large collection of slower co-processors that can simply apply the same computation on different parts of relatively smaller data, but the SPMD parallelism ends up with an efficient way to obtain final solutions (i.e. time to solution is shorter).
Programming with Big Data in R : Programming with pbdR requires usage of various packages developed by pbdR core team. Packages developed are the following. Among these packages, pbdMPI provides wrapper functions to MPI library, and it also produces a shared library and a configuration file for MPI environments. All other packages rely on this configuration for installation and library loading that avoids difficulty of library linking and compiling. All other packages can directly use MPI functions easily. pbdMPI --- an efficient interface to MPI either OpenMPI or MPICH2 with a focus on Single Program/Multiple Data (SPMD) parallel programming style pbdSLAP --- bundles scalable dense linear algebra libraries in double precision for R, based on ScaLAPACK version 2.0.2 which includes several scalable linear algebra packages (namely BLACS, PBLAS, and ScaLAPACK). pbdNCDF4 --- interface to Parallel Unidata NetCDF4 format data files pbdBASE --- low-level ScaLAPACK codes and wrappers pbdDMAT --- distributed matrix classes and computational methods, with a focus on linear algebra and statistics pbdDEMO --- set of package demonstrations and examples, and this unifying vignette pmclust --- parallel model-based clustering using pbdR pbdPROF --- profiling package for MPI codes and visualization of parsed stats pbdZMQ --- interface to ØMQ remoter --- R client with remote R servers pbdCS --- pbdR client with remote pbdR servers pbdRPC --- remote procedure call kazaam --- very tall and skinny distributed matrices pbdML --- machine learning toolbox Among those packages, the pbdDEMO package is a collection of 20+ package demos which offer example uses of the various pbdR packages, and contains a vignette that offers detailed explanations for the demos and provides some mathematical or statistical insight.
Programming with Big Data in R : Raim, A.M. (2013). Introduction to distributed computing with pbdR at the UMBC High Performance Computing Facility (PDF) (Technical report). UMBC High Performance Computing Facility, University of Maryland, Baltimore County. HPCF-2013-2. Archived from the original (PDF) on 2014-02-04. Retrieved 2013-06-26. Bachmann, M.G., Dyas, A.D., Kilmer, S.C. and Sass, J. (2013). Block Cyclic Distribution of Data in pbdR and its Effects on Computational Efficiency (PDF) (Technical report). UMBC High Performance Computing Facility, University of Maryland, Baltimore County. HPCF-2013-11. Archived from the original (PDF) on 2014-02-04. Retrieved 2014-02-01.: CS1 maint: multiple names: authors list (link) Bailey, W.J., Chambless, C.A., Cho, B.M. and Smith, J.D. (2013). Identifying Nonlinear Correlations in High Dimensional Data with Application to Protein Molecular Dynamics Simulations (PDF) (Technical report). UMBC High Performance Computing Facility, University of Maryland, Baltimore County. HPCF-2013-12. Archived from the original (PDF) on 2014-02-04. Retrieved 2014-02-01.: CS1 maint: multiple names: authors list (link) Dirk Eddelbuettel (13 November 2022). "High-Performance and Parallel Computing with R". "R at 12,000 Cores".This article was read 22,584 times in 2012 since it posted on October 16, 2012, and ranked number 3 Google Summer of Code - R 2013. "Profiling Tools for Parallel Computing with R". Archived from the original on 2013-06-29. : \|author= has generic name (help)CS1 maint: numeric names: authors list (link) Wush Wu (2014). "在雲端運算環境使用R和MPI".: CS1 maint: numeric names: authors list (link) Wush Wu (2013). "快速在AWS建立R和pbdMPI的使用環境". YouTube.: CS1 maint: numeric names: authors list (link)
Picollator : Picollator is an Internet search engine that performs searches for web sites and multimedia by visual query (image) or text, or a combination of visual query and text. Picollator recognizes objects in the image, obtains their relevance to the text and vice versa, and searches in accordance with all information provided.
Picollator : Picollator identifies human faces in the images and creates a database of people's faces. This allows the user to search for other images of the submitted person, lookalikes and/or similar images in images found on websites. Picollator can be used in any language.
Picollator : 2006 – Recogmission LLC developed a desktop application for photo collections management. The system automatically classifies, manages and retrieves photographs stored locally or in corporate databases. 2007 – Recogmission started Picollator multimedia search engine project, now in Beta stage. 2008 – Picollator.mobi is launched—a new universal search engine for mobile phones. 2009 – Recogmission opens the web based content filter service piFilter.com, which inherited some pattern recognition technologies from Picollator.
Picollator : Most image search engines match user textual query and picture tags. Picollator is based on a different approach. Patterns and objects found in the image are stored in its database, therefore it is able to recognise the contents of the image and compare it to other images to find similarities. To search for multimedia information, the user may submit Sample image to find images with relevant people Image to find web resources Text to find images Text to find web resources Text and Image to find images and/or web resources.
Picollator : ru:Recogmission LLC has developed an indexing engine for multimedia information search based on the visual query. Recogmission develops solutions for multimedia information (image, text and video) indexing and searching on the web and in corporate environments.
Picollator : Picollator Loves My Girlish Smile, Inside the Marketers Studio - David Berkowitz's Marketing Blog Archived 2008-06-12 at the Wayback Machine. March 20, 2008 Picollator - Image search engine, Phil Bradley's weblog. March 31, 2008 Image-Based Queries, Techpin. March 16, 2008 Picollator: Buscador de rostros, Neoteo Archived 2008-08-20 at the Wayback Machine Picollator Online Search, buscador de caras dentro de las imágenes online, Genbeta Archived 2008-07-09 at the Wayback Machine. March 26, 2008 Picollator:基于脸部特征的图片搜索引擎, 天涯海阁 Archived 2008-08-05 at the Wayback Machine Picollator - Gesichter suchen mit Bildvorlagen, @-web Suchmaschinen Weblog. July 21, 2008 Picollator Launches New Web Search System, TMCnet.com Archived 2008-08-08 at the Wayback Machine. July 15, 2008 Picollator sucht Menschen mithilfe von Gesichtserkennung, Internet World Business Archived 2008-10-04 at the Wayback Machine. July 22, 2008 Sketch based query for image retrieval, Kmvirtual Archived 2009-03-01 at the Wayback Machine. May 30, 2008 Picollator - текстовый или визуальный поиск?, Стартаперы.ru Picollator ищет похожих людей, Новости Медиа Атлас Archived 2011-10-02 at the Wayback Machine. March 3, 2008 Picollator - Οπτική αναζήτηση… περίπου :), Internetakias.gr Archived 2008-10-02 at the Wayback Machine. April 7, 2008 Доступны мультимедийные запросы в новой поисковой системе Picollator, ITua.info. March 14, 2008 Как найти человека по рисунку лица?, Newsland Всевидящее око онлайна, Игромания. July, 2008 Microsoft поднимет долю программ в ВВП, OSP.ru July 1, 2008 Система поиска по мультимедийным запросам, Мониторинг Интернета. April 7, 2008 В Рунете запущена система мультимедийного поиска Picollator.ru, Elvisti.com. April 13, 2008 Мультимедийный поиск становится интеллектуальным, Rocit.ru Archived 2016-03-06 at the Wayback Machine. July 2, 2008 В Рунете появилась система поиска похожих картинок, Eplus.com.ua Archived 2008-07-25 at the Wayback Machine. March 11, 2008 Ресурсы в сети можно искать с помощью изображений, Commcenter.ru. March 29, 2008
Piranha (software) : Piranha is a text mining system. It was developed for the United States Department of Energy (DOE) by Oak Ridge National Laboratory (ORNL). The software processes free-text documents and shows relationships amongst them, a technique valuable across numerous data domains, from health care fraud to national security. The results are presented in clusters of prioritized relevance. Piranha uses the term frequency/inverse corpus frequency term weighting method which provides strong parallel processing of textual information, thus the ability to analyze large document sets. Piranha has six main elements: Collecting and Extracting: Millions of documents from sources such as databases and social media can be collected and text extracted from hundreds of file formats; This information can be translated to other languages. Storing and indexing: Documents in search servers, relational databases, etc. can be stored and indexed. Recommending: The system can highlight the most valuable information for specific users. Categorizing: Grouping items via supervised and semi-supervised machine learning methods and targeted search lists. Clustering: Similarity is used to group documents hierarchically. Visualizing: Showing relationships among documents so that users can quickly recognize connections. This work has resulted in eight patents (9,256,649, 8,825,710, 8,473,314, 7,937,389, 7,805,446, 7,693,9037, 7,315,858, 7,072,883), and commercial licenses (including TextOre and Pro2Serve), a spin-off company with the inventors, Covenant Health, and Pro2Serve called VortexT Analytics, two R&D 100 Awards, and scores of peer reviewed research publications.
Piranha (software) : Cui, X., Beaver, J., St. Charles, J., Potok, T. (September 2008). Proceedings of the IEEE Swarm Intelligence Symposium, St. Louis, Mo. Dimensionality Reduction for High Dimensional Particle Swarm Clustering. Yasin, Rutrell (Nov 29, 2012) GCN. Energy lab's Piranha puts teeth into text analysis Franklin Jr., Curtis (Nov 30, 2012) Enterprise Efficiency. Piranha Brings Affordable Big-Data to Government Breeden II, John (Dec 7, 2012) GCN. Swimming with Piranha: Testing Oak Ridge's text analysis tool Kirby, Bob (Summer 2013) FedTech. Big Data Can Help the Federal Government Move Mountains. Here's How. R. M. Patton, B. G. Beckerman, T. E. Potok, G. Tourassi, "A Recommender System for Web-Based Discovery and Refinement of Information Radiologists Seek", Radiological Society of North America (RSNA), 2012 Annual Meeting, Nov. 2012, Chicago, IL, USA. R. M. Patton, T. E. Potok, B. A. Worley, "Discovery & Refinement of Scientific Information via a Recommender System", The Second International Conference on Advanced Communications and Computation, Oct. 2012, Venice, Italy. J. W. Reed, T. E. Potok, and R. M. Patton, "A multi-agent system for distributed cluster analysis," in Proceedings of Third International Workshop on Software Engineering for Large-Scale Multi- Agent Systems (SELMAS'04)" W16L Workshop - 26th International Conference on Software Engineering Edinburgh, Scotland, UK: IEE, 2004, pp. 152-5. J. Reed, Y. Jiao, T. E. Potok, B. Klump, M. Elmore, and A. R. Hurson, "TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams," in Proceedings of 5th International Conference on Machine Learning and Applications (ICMLA'06). vol. 0 ORLANDO, FL, 2006, pp. 258–263.
Piranha (software) : 2007 R&D 100 Magazine's Award Piranha (software)
Piranha (software) : U.S. patent 7,072,883 – System for gathering and summarizing internet information U.S. patent 7,315,858 – Method for gathering and summarizing internet information U.S. patent 7,693,903 U.S. patent 7,805,446 – Agent-based method for distributed clustering of textual information U.S. patent 7,937,389 – Dynamic reduction of dimensions of a document vector in a document search and retrieval system U.S. patent 8,473,314 – Method and system for determining precursors of health abnormalities from processing medical records
Piranha (software) : DOE Energy Innovlation Portal (2014) Agent-Based Software for Gathering and Summarizing Textual and Internet Information. ORNL Piranha website
R (programming language) : R is a programming language for statistical computing and data visualization. It's been adopted in the fields of data mining, bioinformatics and data analysis. The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software. R is licensed by the GNU Project and available under the GNU General Public License. It's written primarily in C, Fortran, and R itself. Precompiled executables are provided for various operating systems. As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface.
R (programming language) : R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland. The language was inspired by the S programming language, with most S programs able to run unaltered in R. The language was also inspired by Scheme's lexical scoping, allowing for local variables. The name of the language, R, comes from being both an S language successor as well as the shared first letter of the authors, Ross and Robert. In August 1993, Ihaka and Gentleman posted a binary of R on StatLib — a data archive website. At the same time, they announced the posting on the s-news mailing list. On 5 December 1997, R became a GNU project when version 0.60 was released. On 29 February 2000, the 1.0 version was released.
R (programming language) : R packages are collections of functions, documentation, and data that expand R. For example, packages add report features such as RMarkdown, Quarto, knitr and Sweave. Packages also add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests, spatial analysis, time-series analysis, and clustering. Easy package installation and use have contributed to the language's adoption in data science. Base packages are immediately available when starting R and provide the necessary syntax and commands for programming, computing, graphics production, basic arithmetic, and statistical functionality. The Comprehensive R Archive Network (CRAN) was founded in 1997 by Kurt Hornik and Friedrich Leisch to host R's source code, executable files, documentation, and user-created packages. Its name and scope mimic the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network. CRAN originally had three mirrors and 12 contributed packages. As of 16 October 2024, it has 99 mirrors and 21,513 contributed packages. Packages are also available on repositories R-Forge, Omegahat, and GitHub. The Task Views on the CRAN web site list packages in fields such as causal inference, finance, genetics, high-performance computing, machine learning, medical imaging, meta-analysis, social sciences, and spatial statistics. The Bioconductor project provides packages for genomic data analysis, complementary DNA, microarray, and high-throughput sequencing methods. The tidyverse package bundles several subsidiary packages that provide a common interface for tasks related to accessing and processing "tidy data", data contained in a two-dimensional table with a single row for each observation and a single column for each variable. Installing a package occurs only once. For example, to install the tidyverse package: To load the functions, data, and documentation of a package, one executes the library() function. To load tidyverse:
R (programming language) : R comes installed with a command line console. Available for installation are various integrated development environments (IDE). IDEs for R include R.app (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R. General purpose IDEs that support R include Eclipse via the StatET plugin and Visual Studio via R Tools for Visual Studio. Editors that support R include Emacs, Vim via the Nvim-R plugin, Kate, LyX via Sweave, WinEdt (website), and Jupyter (website). Scripting languages that support R include Python (website), Perl (website), Ruby (source code), F# (website), and Julia (source code). General purpose programming languages that support R include Java via the Rserve socket server, and .NET C# (website). Statistical frameworks which use R in the background include Jamovi and JASP.
R (programming language) : The R Core Team was founded in 1997 to maintain the R source code. The R Foundation for Statistical Computing was founded in April 2003 to provide financial support. The R Consortium is a Linux Foundation project to develop R infrastructure. The R Journal is an open access, academic journal which features short to medium-length articles on the use and development of R. It includes articles on packages, programming tips, CRAN news, and foundation news. The R community hosts many conferences and in-person meetups - see the community maintained GitHub list. These groups include: UseR!: an annual international R user conference (website) Directions in Statistical Computing (DSC) (website) R-Ladies: an organization to promote gender diversity in the R community (website) SatRdays: R-focused conferences held on Saturdays (website) R Conference (website) posit::conf (formerly known as rstudio::conf) (website)
R (programming language) : The main R implementation is written primarily in C, Fortran, and R itself. Other implementations include: pretty quick R (pqR), by Radford M. Neal, attempts to improve memory management. Renjin is an implementation of R for the Java Virtual Machine. CXXR and Riposte are implementations of R written in C++. Oracle's FastR is an implementation of R, built on GraalVM. TIBCO Software, creator of S-PLUS, wrote TERR — an R implementation to integrate with Spotfire. Microsoft R Open (MRO) was an R implementation. As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution.
R (programming language) : Although R is an open-source project, some companies provide commercial support: Oracle provides commercial support for the Big Data Appliance, which integrates R into its other products. IBM provides commercial support for in-Hadoop execution of R.
R (programming language) : All R version releases from 2.14.0 onward have codenames that make reference to Peanuts comics and films. In 2018, core R developer Peter Dalgaard presented a history of R releases since 1997. Some notable early releases before the named releases include: Version 1.0.0 released on 29 February 2000 (2000-02-29), a leap day Version 2.0.0 released on 4 October 2004 (2004-10-04), "which at least had a nice ring to it" The idea of naming R version releases was inspired by the Debian and Ubuntu version naming system. Dalgaard also noted that another reason for the use of Peanuts references for R codenames is because, "everyone in statistics is a P-nut".
R (programming language) : Comparison of numerical-analysis software Comparison of statistical packages List of numerical-analysis software List of statistical software Rmetrics
R (programming language) : Wickham, Hadley; Çetinkaya-Rundel, Mine; Grolemund, Garrett (2023). R for data science: import, tidy, transform, visualize, and model data (2nd ed.). Beijing Boston Farnham Sebastopol Tokyo: O'Reilly. ISBN 978-1-4920-9740-2. Gagolewski, Marek (2024). Deep R Programming. doi:10.5281/ZENODO.7490464. ISBN 978-0-6455719-2-9.
R (programming language) : R Technical Papers Big Book of R, curated list of R-related programming books Books Related to R - R Project, partially annotated curated list of books relating to R or S.
RapidMiner : RapidMiner is a data science platform that analyses the collective impact of an organization's data. It was acquired by Altair Engineering in September 2022.
RapidMiner : RapidMiner, formerly known as YALE (Yet Another Learning Environment), was developed by Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer in 2001 at the Artificial Intelligence Unit of the Technical University of Dortmund. Starting in 2006, its development was driven by Rapid-I, a company founded by Ingo Mierswa and Ralf Klinkenberg in the same year. In 2013, the company rebranded from Rapid-I to RapidMiner.
RapidMiner : RapidMiner uses a client/server model with the server offered either on-premises or in public or private cloud infrastructures. RapidMiner provides data mining and machine learning procedures including: data loading and transformation (ETL), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment. RapidMiner is written in the Java programming language. RapidMiner provides a GUI to design and execute analytical workflows. Those workflows are called “Processes” in RapidMiner and they consist of multiple “Operators”. Each operator performs a single task within the process, and the output of each operator forms the input of the next one. Alternatively, the engine can be called from other programs or used as an API. Individual functions can be called from the command line. RapidMiner provides a variety of learning schemes, models, and algorithms that can be extended using R and Python scripts. RapidMiner can also use plugins available through the RapidMiner Marketplace. The RapidMiner Marketplace is a platform for developers to create data analysis algorithms and publish them to the community. The RapidMiner Studio Free Edition, which is limited to one logical processor and 10,000 data rows, is available under the AGPL license.
RapidMiner : The report noted that RapidMiner provides deep and broad modeling capabilities for automated end-to-end model development. In the 2018 annual software poll, KD-nuggets readers voted RapidMiner as one of the most popular data analytics software with the poll’s respondents citing the software package as the tool they use. RapidMiner has received millions of total downloads and has over 400,000 users including BMW, Intel, Cisco, GE, and Samsung as paying customers. RapidMiner claims to be the market leader in the software for data science platforms against competitors such as SAS and IBM.
RapidMiner : About 50 developers worldwide participated in the development of the open-source RapidMiner with the majority of the contributors being employees of RapidMiner. The company that develops RapidMiner received a $16 million Series C funding with participation from venture capital firms Nokia Growth Partners, Ascent Venture Partners, Longworth Venture Partners, Earlybird Venture Capital and Open-Ocean. Open-Ocean partner Michael "Monty" Widenius is one of the founders of MySQL.
RapidMiner : The RapidMiner data science platform consists of the following main components: RapidMiner Studio, RapidMiner AI Hub and RapidMiner Go which can be deployed as a part of the AI Hub. This video explains the links between the main elements and advises on the suitability of each component for different user groups and use cases.
RapidMiner : Official website
Rattle GUI : Rattle GUI is a free and open source software (GNU GPL v2) package providing a graphical user interface (GUI) for data mining using the R statistical programming language. Rattle is used in a variety of situations. Currently there are 15 different government departments in Australia, in addition to various other organisations around the world, which use Rattle in their data mining activities and as a statistical package. Rattle provides considerable data mining functionality by exposing the power of the R Statistical Software through a graphical user interface. Rattle is also used as a teaching facility to learn the R software Language. There is a Log Code tab, which replicates the R code for any activity undertaken in the GUI, which can be copied and pasted. Rattle can be used for statistical analysis, or model generation. Rattle allows for the dataset to be partitioned into training, validation and testing. The dataset can be viewed and edited. There is also an option for scoring an external data file.
Rattle GUI : File Inputs = CSV, TXT, Excel, ARFF, ODBC, R Dataset, RData File, Library Packages Datasets, Corpus, and Scripts. Statistics = Min, Max, Quartiles, Mean, St Dev, Missing, Medium, Sum, Variance, Skewness, Kurtosis, chi square. Statistical tests = Correlation, Wilcoxon-Smirnov, Wilcoxon Rank Sum, T-Test, F-Test, and Wilcoxon Signed Rank. Clustering = KMeans, Clara, Hierarchical, and BiCluster. Modeling = Decision Trees, Random Forests, ADA Boost, Support Vector Machine, Logistic Regression, and Neural Net. Evaluation = Confusion Matrix, Risk Charts, Cost Curve, Hand, Lift, ROC, Precision, Sensitivity. Charts = Box Plot, Histogram, Correlations, Dendrograms, Cumulative, Principal Components, Benford, Bar Plot, Dot Plot, and Mosaic. Transformations = Rescale (Recenter, Scale 0-1, Median/MAD, Natural Log, and Matrix) - Impute ( Zero/Missing, Mean, Median, Mode & Constant), Recode (Binning, Kmeans, Equal Widths, Indicator, Join Categories) - Cleanup (Delete Ignored, Delete Selected, Delete Missing, Delete Obs with Missing) Rattle also uses two external graphical investigation / plotting tools. Latticist and GGobi are independent applications which provide highly dynamic and interactive graphic data visualisation for exploratory data analysis.
Rattle GUI : The capabilities of R are extended through user-submitted packages, which allow specialized statistical techniques, graphical devices, as well as import/export capabilities to many external data formats. Rattle uses these packages - RGtk2, pmml, colorspace, ada, amap, arules, biclust, cba, descr, doBy, e1071, ellipse, fEcofin, fBasics, foreign, fpc, gdata, gtools, gplots, gWidgetsRGtk2, Hmisc, kernlab, latticist, Matrix, mice, network, nnet, odfWeave, party, playwith, psych, randomForest, reshape, RGtk2Extras, ROCR, RODBC, rpart, RSvgDevice, survival, timeDate, graph, RBGL, bitops,
Rattle GUI : R interfaces
Rattle GUI : Graham J Williams (2011). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery, Springer, Use R!. In 2010, Rattle was listed in the top 10 graphical user interfaces in statistical software by Decision Stats. Rattle is described as an "attractive, easy-to-use front end ... data mining toolkit" in an article published in the Teradata Magazine, volume 9, issue 3, page 57 (September 2009). Graham J William (2009). Rattle: A Data Mining GUI for R. The R Journal 1(2):45-55.
Rattle GUI : Official home page Source code page
Renjin : Renjin is an implementation of the R programming language atop the Java Virtual Machine. It is free software released under the GPL. Renjin is tightly integrated with Java to allow the embedding of the interpreter into any Java application with full two-way access between the Java and R code. Renjin's development is primarily supported by BeDataDriven, but ultimately made possible by several current and past contributors including Mehmet Hakan Satman, Hannes Mühleisen, and Ruslan Shevchenko.
Renjin : Renjin's roots lie in an abortive 2010 attempt to compile the GNU R interpreter for the JVM via nestedvm, a toolchain which involves cross-compiling C and Fortran code to a static MIPS binary, which nestedvm can then translate to JVM bytecode. This proved challenging as GNU R had grown to rely heavily on dynamic linking and the best C standard library implementation available at the time for the MIPS architecture, Newlib, was not fully compatible with the GNU C Library, against which GNU R had been developed. The experience with the R4JVM project provided the BeDataDriven team with in depth look at the GNU R codebase, and convinced them that a new implementation, written in Java, was a feasible undertaking. Development on Renjin began in October 2010, and rapidly resulted in a functional, if minimal, interpreter for the R language.
Renjin : Official website
SAS (software) : SAS (previously "Statistical Analysis System") is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics. SAS' analytical software is built upon artificial intelligence and utilizes machine learning, deep learning and generative AI to manage and model data. The software is widely used in industries such as finance, insurance, health care and education. SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated. SAS was further developed in the 1980s and 1990s with the addition of new statistical procedures, additional components and the introduction of JMP. A point-and-click interface was added in version 9 in 2004. A social media analytics product was added in 2010.
SAS (software) : SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and more through the SAS language. SAS programs have DATA steps, which retrieve and manipulate data, PROC (procedures) which analyze the data, and may also have functions. Each step consists of a series of statements. The DATA step has executable statements that result in the software taking an action, and declarative statements that provide instructions to read a data set or alter the data's appearance. The DATA step has two phases: compilation and execution. In the compilation phase, declarative statements are processed and syntax errors are identified. Afterwards, the execution phase processes each executable statement sequentially. Data sets are organized into tables with rows called "observations" and columns called "variables". Additionally, each piece of data has a descriptor and a value. PROC statements call upon named procedures. Procedures perform analysis and reporting on data sets to produce statistics, analyses, and graphics. There are more than 300 named procedures and each one performs a substantial body of statistical work. PROC statements can also display results, sort data or perform other operations. SAS macros are pieces of code or variables that are coded once and referenced to perform repetitive tasks. SAS data can be published in HTML, PDF, Excel, RTF and other formats using the Output Delivery System, which was first introduced in 2007. SAS Enterprise Guide is SAS's point-and-click interface. It generates code to manipulate data or perform analysis without the use of the SAS programming language. The SAS software suite has more than 200 add-on packages, sometimes called components Some of these SAS components, i.e. add on packages to Base SAS include:
SAS (software) : As of 2011, SAS's largest set of products was its line for customer intelligence. Numerous SAS modules for web, social media and marketing analytics may be used to profile customers and prospects, predict their behaviors and manage and optimize communications. SAS also provides the SAS Fraud Framework. The framework's primary functionality is to monitor transactions across different applications, networks and partners and use analytics to identify anomalies that are indicative of fraud. This software uses artificial intelligence to monitor income and assets. The SAS Asset and Liability Management platform utilizes generative AI and machine learning to monitor risk and model risk management strategies. SAS Governance, Risk and Compliance Manager provides risk modeling, scenario analysis, and other functions in order to manage and visualize risk, compliance and corporate policies. There is also a SAS Enterprise Risk Management product-set designed primarily for banks and financial services organizations. SAS products for monitoring and managing the operations of IT systems are collectively referred to as SAS IT Management Solutions. SAS collects data from various IT assets on performance and utilization, then creates reports and analyses. SAS's Performance Management products consolidate and provide graphical displays for key performance indicators (KPIs) at the employee, department and organizational level. The SAS Supply Chain Intelligence product suite is offered for supply chain needs, such as forecasting product demand, managing distribution and inventory and optimizing pricing. There is also a "SAS for Sustainability Management" set of software to forecast environmental, social and economic effects and identify causal relationships between operations and their impact on the environment or ecosystem. SAS has various analytical tools related to risk management. The SAS Asset and Liability Management platform utilizes generative AI and machine learning to monitor risk and model risk management strategies. SAS has products for specific industries, such as government, retail, telecommunications, aerospace, marketing optimization, and high-performance computing. The company has a suite of analytical products related to health care and life sciences.
SAS (software) : According to IDC, SAS is the largest market-share holder in "advanced analytics" with 35.4 percent of the market as of 2013. It is the fifth largest market-share holder for business intelligence (BI) software with a 6.9% share and the largest independent vendor. It competes in the BI market against SAP BusinessObjects, IBM Cognos, SPSS Modeler, Oracle Hyperion, and Microsoft Power BI. SAS has been named in the Gartner Leader's Quadrant for Data Integration Tools and for Business Intelligence and Analytical Platforms. A study published in 2011 in BMC Health Services Research found that SAS was used in 42.6 percent of data analyses in health service research, based on a sample of 1,139 articles drawn from three journals.
SAS (software) : Comparison of numerical-analysis software Comparison of OLAP servers JMP (statistical software), a subsidiary of SAS Institute Inc. SAS language R (programming language)
SAS (software) : Greenberg, Bernard G.; Cox, Gertrude M.; Mason, David D.; Grizzle, James E.; Johnson, Norman L.; Jones, Lyle V.; Monroe, John; Simmons, Gordon D. Jr. (1978). Nourse, E. Shepley (ed.). "Statistical Training and Research: The University of North Carolina System". International Statistical Review. 46 (2): 171–207. doi:10.2307/1402812. JSTOR 1402812. Wikiversity:Data Analysis using the SAS Language
SAS (software) : Official website SAS OnDemand for Academics No-cost access for learners (free SAS Profile required) A Glossary of SAS terminology SAS for Developers SAS community forums
SAS Viya : SAS Viya is an artificial intelligence, analytics and data management platform developed by SAS Institute.
SAS Viya : SAS Viya was released in 2016. The software was containerized with the release of Viya 4 in 2020.
SAS Viya : The platform is cloud-native, and is executed on SAS's Cloud Analytics Services (CAS) engine. It is compatible with open source software, allowing users to build models using open sources tool such as R, Python and Jupyter. It integrates with major large language models like GPT-4 and Gemini Pro. The platform uses econometrics to create predictive models for forecasting scenarios based on complex data. It also has features for detecting algorithmic bias, auditing decisions and monitoring models. SAS Viya has released software as a service (SaaS) modules for creating AI content. These include Viya Workbench, used for AI models, and Viya App Factory, for creating AI applications. Users can develop models in Viya Workbench using either a Visual Studio Code interface or Jupyter. Viya Copilot, a generative AI virtual assistant, was released in April 2024. The software is available on Amazon AWS Marketplace, Google Cloud, Red Hat OpenShift, and on Microsoft Azure Marketplace under a pay-as-you-use model.
SAS Viya : SAS (software) == References ==
Scikit-learn : scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
Scikit-learn : The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately developed and distributed third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub.
Scikit-learn : Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting
Scikit-learn : Fitting a random forest classifier:
Scikit-learn : scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible. scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more.

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