A New Method for Fuzzy Clustering Analysis Based on AFS Fuzzy Logic

Abstract

In this paper, first, the AFS fuzzy logic clustering algorithm has been studied further. Then, based on the fuzzy implicator, an algorithm of selecting optimal subsets of relevant features for fuzzy clustering is proposed. Thus a new AFS fuzzy logic clustering algorithm is achieved. Finally, the proposed clustering algorithm is applied to the well known real-world wine data set. Experimental results demonstrate that a high clustering accuracy can be obtained by the proposed clustering algorithm only according to the order relations of the attributes, in stead of the numerical representations of the attributes. The proposed clustering algorithm can be applied to the data sets with various data types such as real numbers, Boolean values, partial orders, even human intuition descriptions.