Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data

We develop a three-step fuzzy logic-based algorithm for clustering categorical attributes, and we apply it to analyze cultural data. In the first step the algorithm employs an entropy-based clustering scheme, which initializes the cluster centers. In the second step we apply the fuzzy c-modes algorithm to obtain a fuzzy partition of the data set, and the third step introduces a novel cluster validity index, which decides the final number of clusters.

• George E. Tsekouras
• Dimitris Papageorgiou
• Sotiris Kotsiantis
• Christos Kalloniatis
• Panagiotis Pintelas

• Categorical data
• cultural data
• fuzzy logic clustering
• fuzzy c-modes
• cluster validity index.

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