Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)

Abstract

 Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.

Authors and Affiliations

Kohei Arai, Cahya Rahmad

Keywords

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  • EP ID EP93387
  • DOI -
  • Views 119
  • Downloads 0

How To Cite

Kohei Arai, Cahya Rahmad (2013). Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other). International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(7), 1-5. https://europub.co.uk/articles/-A-93387