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Extracting fuzzy classifications rules from three - way data

Kacprzyk J., Owsinski J. W., Viattchenin D. A.

Journal of Automation Mobile Robotics and Intelligent Systems

2014

Vol. 8, No. 2

47--57

The paper deals in the conceptual way with the problem of extracting fuzzy classification rules from the three-way data meant in the sense of Sato and Sato [7]. A novel technique based on a heuristic method of possibilistic clustering is proposed. A description of basic concepts of a heuristic method of possibilistic clustering based on concept of an allotment is provided. A preprocessing technique for three-way data is shown. An extended method of constructing fuzzy classification rules based on clustering results is proposed. An illustrative example of the method’s application to the Sato and Sato’s data [7] is provided. An analysis of the experimental results obtained with some conclusions are given.

Bacelar-Nicolau H., Nicolau F. F., Sousa A., Bacelar-Nicolau L.

Biocybernetics and Biomedical Engineering

2009

Vol. 29, no. 2

9-18

Cluster analysis or classification usually concerns a set of exploratory multivariate data analysis methods and techniques for finding a clustering structure on a dataset. That may refer either to groups of statistical data units or to groups of variables. In this work we deal with a generalization of this paradigm concerning clustering of complex data described by three different types of variables, frequently present in a three-way context. We obtain compatible versions of the same affinity coefficient for measuring similarity between statistical data units described by those three types of variables. A global generalized similarity coefficient is analyzed for such kind of mixed data, often arising in data mining or knowledge mining.