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Model-based biclustering of clickstream data

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  • Melnykov, Volodymyr

Abstract

Navigation patterns expressed by sequences of visited web-sites or categories can characterize the behavior and habits of users. Such web-page routes taken by individuals are commonly called clickstreams. Clustering clickstream sequences is a recent yet challenging problem with many applications. The main difficulty is related to the fact that one needs to group categorical data sequences rather than vectors and the majority of traditional clustering algorithms are not applicable in this setting. The time-related character of data suggests that dynamic models have a better promise than static ones. Model-based clustering relying on the mixture of first order Markov models will be considered. Since the number of distinct web-pages, and therefore the number of states in a Markov process, can be very high, such a mixture model involves a large number of parameters. Thus, grouping states by their similarity to reduce the number of parameters in the model is also proposed. Then, states are clustered along with users providing a biclustering framework. The developed methodology is illustrated on synthetic and real datasets with good results.

Suggested Citation

  • Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
    • Handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:31-45
    DOI: 10.1016/j.csda.2014.09.016
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    References listed on IDEAS

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    Cited by:

    1. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    2. Yana Melnykov & Xuwen Zhu & Volodymyr Melnykov, 2021. "Transformation mixture modeling for skewed data groups with heavy tails and scatter," Computational Statistics, Springer, vol. 36(1), pages 61-78, March.
    3. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    4. Faicel Chamroukhi, 2016. "Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 374-411, October.
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    6. Melnykov, Volodymyr & Zhu, Xuwen, 2018. "On model-based clustering of skewed matrix data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 181-194.

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