Some studies on fuzzy clustering of psychosis data
by Subhagata Chattopadhyay, Dilip Kumar Pratihar, S.C. De Sarkar
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 2, 2007

Abstract: Clustering is a well-known method of data mining, which aims at extracting useful information from a data set. Clusters could be either crisp (having well-defined boundaries) or fuzzy (with vague boundaries) in nature. The present paper deals with fuzzy clustering of psychosis data. A set of statistically generated psychosis data are clustered using Fuzzy C-Means (FCM) algorithm and entropy-based method and its proposed extensions. From the clusters, we finally decide on patient distributions response-wise. Comparisons are made of the above algorithms, in terms of quality of clusters made and their computational complexity. Finally, the multidimensional best set of clusters are mapped into 2-D for visualisation, using a Self-Organising Map (SOM).

Online publication date: Mon, 04-Jun-2007

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