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A Novel Clustering Approach Using Shape Based Similarity

  • Smriti Srivastava
  • Saurabh Bhardwaj
  • J. R. P. Gupta
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)

Abstract

The present research proposes a paradigm for the clustering of data in which no prior knowledge about the number of clusters is required. Here shape based similarity is used as an index of similarity for clustering. The paper exploits the pattern identification prowess of Hidden Markov Model (HMM) and overcomes few of the problems associated with distance based clustering approaches. In the present research partitioning of data into clusters is done in two steps. In the first step HMM is used for finding the number of clusters then in the second step data is classified into the clusters according to their shape similarity. Experimental results on synthetic datasets and on the Iris dataset show that the proposed algorithm outperforms few commonly used clustering algorithm.

Keywords

Clustering Hidden Markov Model Shape Based similarity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Smriti Srivastava
    • 1
  • Saurabh Bhardwaj
    • 1
  • J. R. P. Gupta
    • 1
  1. 1.Netaji Subhas Institute of TechnologyNew DelhiIndia

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