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)


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.


Clustering Hidden Markov Model Shape Based similarity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, Philadelphia (2007)MATHCrossRefGoogle Scholar
  2. 2.
    Xu, R., Wunsch, D.I.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)CrossRefGoogle Scholar
  3. 3.
    Wang, H., Pei, J.: Clustering by Pattern Similarity. Journal of Computer Science and Technology 23(4), 481–496 (2008)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Smyth, P.: Clustering sequences with hidden Markov models. Advances in Neural Information Processing Systems 9, 648–654 (1997)MathSciNetGoogle Scholar
  5. 5.
    Bicego, M., Murino, V., Figueiredo, M.A.: Similarity-based classification of sequences using hidden Markov models. Pattern Recognition 37(12), 2281–2291 (2004)Google Scholar
  6. 6.
    Hassan, R., Nath, B.: Stock market forecasting using hidden markov model. In: Proceedings of the Fifth International Conference on Intelligent Systems Design and Application, pp. 192–196 (2005)Google Scholar
  7. 7.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. IEEE (77), 257–286 (1989)CrossRefGoogle Scholar
  8. 8.
    Blimes, J.A.: A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Berkeley, California: International Computer Science Institute Technical Report ICSI-TR-97-021 (1998)Google Scholar
  9. 9.
    Srivastava, S., Bhardwaj, S., Madhvan, A., Gupta, J.R.P.: A Novel Shape Based Batching and Prediction approach for Time series using HMMs and FISs. In: 10th International Conference on Intelligent Systems Design and Applications, Cairo, Egypt, pp. 929–934 (2010)Google Scholar
  10. 10.
    Bhardwaj, S., Srivastava, S., Madhvan, A., Gupta, J.R.P.: A Novel Shape Based Batching and Prediction approach for Sunspot Data using HMMs and ANNs. In: India International Conference on Power Electronics, New Delhi, India, pp. 1–5 (2011)Google Scholar
  11. 11.
    Maes, U.S.: Social Information Filtering: Algorithms for automating word of mouth. In: ACM CHI, pp. 210–217 (1995)Google Scholar
  12. 12.
    Xia, S.-X., Han, X.-D., Liu, B., Zhou, Y.: A Sample-Weighted Robust Fuzzy C-Means Clustering Algorithm. Energy Procedia (13), 3924–3931 (2011)CrossRefGoogle Scholar

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

Personalised recommendations