A correlation based stochastic partitional algorithm for accurate cluster analysis. (22nd January 2013)

• Record Type: Journal Article
• Authors: Nanda, Satyasai Jagannath; Pradhan, Pyari Mohan; Panda, Ganapati; Mansinha, Lalu; Tiampo, Kristy F.
• Is Part Of: International journal of signal and imaging systems engineering. Volume 6:Number 1(2013)
• Page Start: 52
• Page End: 58
• Publication Date: 2013-01-22
• Subjects: K–means clustering -- correlation clustering -- clustering accuracy -- clustering stability -- cluster analysis -- stochastic partitional clustering -- Pearson correlation -- similarity measures; Signal processing -- Periodicals; Imaging systems -- Periodicals; Information theory -- Periodicals; 621.3822
• Languages: English
• ISSNs: 1748-0698

Abstract

Most partitional clustering algorithms such as K–means, K–nearest neighbour, evolutionary techniques use distance based similarity measures to group the patterns of a data set. However the distance based algorithms may converge to local optima when there are large variations in the attributes of the data set, leading to improper clustering. In this paper we propose a simple stochastic partitional clustering algorithm based on a Pearson correlation based similarity measure. Experiments on real–life data sets demonstrate that the proposed method provides superior performance compared to distance based K–means algorithm.