uni software plus

machine learning framework
features

Supervised Analysis

  • fuzzy decision trees: FS-ID3, a fuzzy variant of the ID3 learning algorithm to create decision trees
  • fuzzy rule generation: FS-FOIL, a fuzzy variant of Quinlan's FOIL method
  • cluster descriptions: FS-MINER, a proprietary method to find cluster descriptions
  • optimization of fuzzy controllers: RENO, a proprietary method, which uses numerical optimization to find computationally accurate and robust fuzzy rules
  • Ridge Regression: Regression with built-in feature selection.
a fuzzy variant of a decision tree
a SOM Plot

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: creates a fuzzy segmentation of the data
  • Ward clustering: a crisp, agglomerative clustering method

Additional features

  • Powerful functions for routine tasks
  • Automated model testing
  • ODBC Data import
  • Advanced data visualization
  • Fuzzy logic (using different types of fuzzy sets and t-norms)
  • Fuzzy inference (Mamdani, Sugeno, Tagaki-Sugeno-Kang)
advanced scatter plots