Some studies on fuzzy clustering of psychosis data

Authors:
Subhagata Chattopadhyay;Dilip Kumar Pratihar;S.C. De Sarkar
Affiliations:
Asia-Pacific ubiquitous HealthCare Research Center (APuHC), The University of New South Wales (UNSW Asia), 1, Kay Siang Road, 248922, Singapore.;Mechanical Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur-721 302, West Bengal, India.;Kalinga Institute of Industrial Technology, Deemed University, Bhubaneswar 751 024, Orissa, India
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2007

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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).