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Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification

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Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

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Abstract

This paper describes a clustering methodology for temporal data using hidden Markov model(HMM) representation. The proposed method improves upon existing HMM based clustering methods in two ways: (i) it enables HMMs to dynamically change its model structure to obtain a better fit model for data during clustering process, and (ii) it provides objective criterion function to automatically select the clustering partition. The algorithm is presented in terms of four nested levels of searches: (i) the search for the number of clusters in a partition, (ii) the search for the structure for a fixed sized partition, (iii) the search for the HMM structure for each cluster, and (iv) the search for the parameter values for each HMM. Preliminary experiments with artificially generated data demonstrate the effectiveness of the proposed methodology.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA

    Cen Li & Gautam Biswas

Authors
  1. Cen Li
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  2. Gautam Biswas
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand

  2. Leiden Institute for Advanced Computer Science, Leiden University, 2300, RA Leiden, The Netherlands

    Joost N. Kok

  3. Berkeley Initiative in Soft Computing, University of California at Berkeley, 329 Soda Hall, Berkeley, CA, 94720, USA

    Michael R. Berthold

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© 1999 Springer-Verlag Berlin Heidelberg

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Li, C., Biswas, G. (1999). Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_21

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  • DOI: https://doi.org/10.1007/3-540-48412-4_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

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