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

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis, Panayiotis E. Pintelas
• Keywords: Artificial Intelligence, Cluster Validity Index, Fuzzy C-modes, Fuzzy Logic-based Algorithm, IJIT 2004