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machine learning framework
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The main purpose of machine learning framework is to build abstract models from arbitrary data sources. If an explicit target is identified (supervised learning), the framework can be used to create a model for forecasting this target parameter. If no such target parameter is available (unsupervised learning), the framework can identify related items and create models that classify new items according to this segmentation.

New in Version 2

  • Grid enabled to do parallel data mining tasks
  • Powerful kernel methods for classification and regression
  • Integration of user-defined algorithms
  • New meta-learning algorithms for feature selection, boosting, and mixtures of experts
  • Easily build models for time series data
Download a detailed description of the new features of Version 2.

Supervised Analysis

  • Decision trees
    • FS-ID3 is a fuzzy variant of the ID3 learning algorithm to create decision trees.
  • Rule induction
    • FS-FOIL is a fuzzy variant of Quinlan's FOIL method.
    • FS-MINER is a proprietary method from SCCH GmbH to find cluster descriptions.
  • Numerical optimization of fuzzy rules
    • RENO is a proprietary method from SCCH GmbH that uses numerical optimization to find computationally accurate and robust fuzzy rules.

Unsupervised Analysis

  • Self-organizing maps
    • Create two-dimensional plots of high-dimensional data sets.
    • Preprocess large and noisy data sets.
    • Recall one or more missing values in the data.
  • Fuzzy c-means clustering and ward clustering
    • Fuzzy c-means clustering creates a fuzzy segmentation of the data.
    • Ward clustering is a crisp, agglomerative clustering method.


  • Forecasting
    • Includes various inference methods to apply created models onto new cases/samples.
  • Classification
    • Uses straightforward decision trees and rule-based methods to forecast the membership of a new sample to a previously defined set of classes.
  • Logical inference
    • Includes logical inference methods, such as Sugeno and Tagaki-Sugeno-Kang controllers, to predict numerical values using rule bases and decision trees. Self-organizing maps (SOMs) are also able to predict new values in a straightforward way.

All of these methods are highly parameterized. The results can be easily visualized using the Mathematica front end. They can also be modified using the Mathematica language to fine-tune the models.