Analysis of Cross-Cultural Data using Hierarchical Fuzzy Logic Clustering

G.E. Tsekouras, D. Papageorgiou, and C. Kalloniatis (Greece)


Cross-cultural data, ethnocultural identity, categorical data,entropy based-clustering, cluster merging, weighted fuzzy cmodes


We develop a fuzzy-logic based categorical data-clustering algorithm to detect shifting patterns related to cross-cultural adaptation of individuals. The algorithm consists of three main steps, which are independent each other so that the output of one step becomes the input to the next step. The main advantage of the algorithm is the utilization of the weighted fuzzy c-modes, which is an extension of the classical fuzzy c-modes and provides flexibility in detecting the real data structure.

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