Data Mining

• Association Rules Overview
  
• Statistica Automated Neural Networks (SANN) - Neural Networks Overview
  Over the past two decades, there has been an explosion of interest in neural networks. It started with the successful application of this powerful technique across a wide range of problem domains, in areas as diverse as finance, medicine, engineering, geology, and even physics.
• Boosted Trees Overview
  The Statistica Boosted Trees module is a complete implementation of the method usually referred to as stochastic gradient boosting trees [Friedman, 1999a, b; Hastie, Tibshirani, & Friedman, 2001; also known as TreeNet ( ™ Salford Systems, Inc.) and MART ( ™ Jerill, Inc.)]. In Statistica , these techniques can be used for regression-type problems (to predict a continuous dependent variable) as well as classification problems (to predict a categorical dependent variable).
• Generalized EM and k-Means Cluster Analysis Overview
  
• Data Miner Recipes (DMR) Overview
  You can build advanced analytic models to relate one or more target (dependent) quantities to a number of input (independent) predictor variables using Data Miner Recipes (DMR).
• Data Mining Workspaces
  
• Feature Selection and Variable Screening
  
• General CHAID Overview
  
• General Classification and Regression Trees Introductory Overview
  Overview
• Generalized Additive Models Overview
  
• Goodness of Fit Calculations Overview
  
• Independent Component Analysis Overview
  Statistica Independent Component Analysis (ICA), part of the Data Mining suite of analyses, is designed for signal separation using a well established and reliable statistical method known as Independent Component Analysis.
• Interactive Trees (C&RT, CHAID) Overview
  
• Lasso Regression
  
• Model Converter
  
• Multivariate Adaptive Regression Splines (MARSplines) Overview
  
• Optimal Binning for Predictive Data Mining Overview
  
• Predictor Screening Introductory Overview
  
• Feature and Method Selection Computational Details
  Feature Selection with Interaction Effects
• Random Forests Overview
  The STATISTICA Random Forest module is a complete implementation of the Random Forest algorithm developed by Breiman. In STATISTICA, this technique can be used for regression-type problems (to predict a continuous dependent variable) as well as classification problems (to predict a categorical dependent variable).
• Rapid Deployment of Predictive Models Overview
  
• Stepwise Model Builder Overview
  
• Text Mining Overview
  The purpose of the Statistica Text and Document Mining module is to provide powerful tools to process unstructured (textual) information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining (statistical and machine learning) algorithms available in the Statistica system.
• Weight of Evidence (WoE) Overview
  

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