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The Fuzzy Hierarchical Cross-Clustering Algorithm. Improvements and Comparative Study

The aim of this paper is to introduce a way to improve the behavior of the Fuzzy Hierarchical Cross-Clustering Algorithm. The Simultaneous Fuzzy n-Means Algorithm produces fuzzy n-partitions of objects and characteristics, but it does not offer a modality to associate each fuzzy set of objects to a fuzzy set of characteristics. Based on our previous experience we are able to propose such an association. In the second part of the paper we will study the behavior of the Fuzzy Hierarchical Cross-Clustering Algorithm using both the original and the Improved Simultaneous Fuzzy n-Means Algorithm.