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Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.
SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software. Therefore, applying it outside Enterprise Miner may be ambiguous. [3]
JMP Pro is intended for data scientists, and has an emphasis on advanced predictive modelling and model selection. [41] JMP Genomics, used for analyzing and visualizing genomics data, [49] requires a SAS component to operate and can access SAS/Genetics and SAS/STAT procedures or invoke SAS macros. [48]
One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time (i.e. growth curve modeling for longitudinal designs); however, it may also be used for repeated measures data in which time is not a factor.
While SAS was originally developed for data analysis, it became an important language for data storage. [5] SAS is one of the primary languages used for data mining in business intelligence and statistics. [29] According to Gartner's Magic Quadrant and Forrester Research, the SAS Institute is one of the largest vendors of data mining software. [24]
Neural Designer is a data mining software based on deep learning techniques written in C++. Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn. RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka.
SAS, [30] a system of software products for statistics. It includes SAS/IML, [31] a matrix programming language. VisSim is a visual block-diagram language for simulation of nonlinear dynamic systems and model-based embedded development. Its fast ODE engine supports real-time simulation of complex large-scale models.
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.