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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/369
Title: A correlation based stochastic partitional algorithm for accurate cluster analysis
Authors: Nanda S.J.
Pradhan P.M.
Panda G.
Mansinha L.
Tiampo K.F.
Keywords: Accuracy of clustering
Correlation clustering
K-means clustering
Stability of clustering algorithm
Issue Date: 2013
Citation: 3
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. Copyright � 2013 Inderscience Enterprises Ltd.
URI: http://dx.doi.org/10.1504/IJSISE.2013.051504
http://10.10.32.48:8080/jspui/handle/2008/369
Appears in Collections:Research Publications

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