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Semi-supervised Multivariate Sequential Pattern Mining

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New Frontiers in Mining Complex Patterns (NFMCP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9607))

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Abstract

Multivariate sequence analysis is of growing interest for learning on data with numerous correlated time-stamped sequences. It is characterized by correlations among dimensions of multivariate sequences and may not be separately analyzed as multiple independent univariate sequences. On the other hand, labeled data is usually expensive and difficult to obtain in many real-world applications. We present a graph-based semi-supervised learning framework for multivariate sequence classification. The framework explores the correlation within the multivariate sequences, and exploits additional information about the distribution of both labeled and unlabeled data to provide better predictive performance. We also develop an efficient method to extend the graph-based learning approach to out-of-sample prediction. We demonstrate the effectiveness of our approach on real-world multivariate sequence datasets from three domains.

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Author information

Authors and Affiliations

  1. NEC Laboratories Europe, Kurfürsten-Anlage 36, 69115, Heidelberg, Germany

    Zhao Xu, Koichi Funaya & Sergio Leoni

  2. NEC Laboratories America, 4 Independence Way, Suite 200, Princeton, NJ, 08540, USA

    Haifeng Chen

Authors
  1. Zhao Xu
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  2. Koichi Funaya
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  3. Haifeng Chen
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  4. Sergio Leoni
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Corresponding author

Correspondence to Zhao Xu .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  3. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  4. ICAR-CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Xu, Z., Funaya, K., Chen, H., Leoni, S. (2016). Semi-supervised Multivariate Sequential Pattern Mining. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-39315-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39314-8

  • Online ISBN: 978-3-319-39315-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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