Fuzzy rule extraction from topicality and membership partitions

This paper proposes extracting fuzzy rules from data using Fuzzy Possibilistic C-Means and Possibilistic Fuzzy C-Means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin Breast Cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling.

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