Authors: Subhagata Chattopadhyay, Dilip Kumar Pratihar, S.C. De Sarkar
Addresses: 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
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).
Keywords: psychosis data; fuzzy clustering; fuzzy C-means algorithm; FCM; entropy-based algorithm; self-organising map; SOM; data mining; visualisation.
International Journal of Business Intelligence and Data Mining, 2007 Vol.2 No.2, pp.143 - 159
Available online: 04 Jun 2007Full-text access for editors Access for subscribers Purchase this article Comment on this article