Title: A novel iterative partitioning approach for building prime clusters

Authors: Sreeja Ashok; M.V. Judy

Addresses: Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi, India ' Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi, India

Abstract: Cluster analysis is an iterative process of knowledge discovery by segregating the data objects into significant and logical groups. An efficient clustering algorithm will produce groups of similar objects which are tightly bonded within the groups and independent between groups. In this paper, we propose a new iterative, non-parametric, partitioning clustering algorithm called prime equivalence clustering algorithm (PECA) based on computation of distance to subsets of attributes where object values are closer. The right number of clusters and the final partition of the datasets are automatically determined without any prior knowledge. The performance of this algorithm has been studied on benchmark datasets and it is proven better than the well-known clustering algorithms.

Keywords: clustering algorithms; non-parametric clustering; iterative partitioning; cluster validation; prime clusters; cluster analysis.

DOI: 10.1504/IJAIP.2015.073712

International Journal of Advanced Intelligence Paradigms, 2015 Vol.7 No.3/4, pp.313 - 325

Received: 03 Nov 2014
Accepted: 01 May 2015

Published online: 16 Dec 2015 *

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