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.
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References
Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, Philadelphia (2007)
Xu, R., Wunsch, D.I.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Wang, H., Pei, J.: Clustering by Pattern Similarity. Journal of Computer Science and Technology 23(4), 481–496 (2008)
Smyth, P.: Clustering sequences with hidden Markov models. Advances in Neural Information Processing Systems 9, 648–654 (1997)
Bicego, M., Murino, V., Figueiredo, M.A.: Similarity-based classification of sequences using hidden Markov models. Pattern Recognition 37(12), 2281–2291 (2004)
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)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. IEEE (77), 257–286 (1989)
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)
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)
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)
Maes, U.S.: Social Information Filtering: Algorithms for automating word of mouth. In: ACM CHI, pp. 210–217 (1995)
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)
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Srivastava, S., Bhardwaj, S., Gupta, J.R.P. (2013). A Novel Clustering Approach Using Shape Based Similarity. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_3
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DOI: https://doi.org/10.1007/978-3-642-32063-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
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