Data-Mining

  • Independent Component Analysis
  • Generalized Cluster Analysis
  • Association Rules
    Detects relationships or associations between specific values of categorical variables in large data sets.
  • General Classification And Regression Tree Models
  • General CHAID Models
  • Advanced C and RT, CHAID (using Interactive Trees)
  • Boosting Trees
  • Random Forest
  • Generalized Additive Models
  • MARSplines
    Full-featured implementation of Multivariate Adaptive Regression Splines (MARSplines) for regression or classification problems.
  • Machine Learning
  • Rapid Deployment
    Quickly generates predictions from one or more previously trained models based on information stored in industry-standard PMML (Predictive Model Markup Language) deployment code. This information can optionally be written into the current input data file or database [if the current input data is a query into an external database for In-Place Database Processing (IDP)] for subsequent analyses involving other variables in the current input data file or data warehouse. PMML is an XML-based language for encoding information (results) from data mining projects. The Rapid Deployment of Predictive Models module is particularly well suited for generating predictions for a large number of observations (cases) because it passes (reads) through the data once, storing only the data for a single observation at a time.
  • Goodness of Fit
    Goodness of fit, classification, prediction; computes various goodness of fit indices based on a variable containing observed values or classification, and one or more variables containing predicted values or classifications. Both continuous variables (for regression-type problems) and categorical variables (for classification problems) can be analyzed. Various goodness of fit measures are available for classification and regression-type problems.
  • Feature Selection and Root Cause Analysis
    The program will use the Statistica Feature Selection and Variable Screening module to select a subset of k best predictors for each variable. The search will be performed predictor-by-predictor, i.e., this is node will perform a fast first-order (no-interactions considered) search of potential predictors of the outcome variable(s) of interest.
  • Combining Groups