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Clustering from Categorical Data Sequences

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  • Harry Crane

Abstract

The three-parameter cluster model is a combinatorial stochastic process that generates categorical response sequences by randomly perturbing a fixed clustering parameter. This clear relationship between the observed data and the underlying clustering is particularly attractive in cluster analysis, in which supervised learning is a common goal and missing data is a familiar issue. The model is well equipped for this task, as it can handle missing data, perform out-of-sample inference, and accommodate both independent and dependent data sequences. Moreover, its clustering parameter lies in the unrestricted space of partitions, so that the number of clusters need not be specified beforehand. We establish these and other theoretical properties and also demonstrate the model on datasets from epidemiology, genetics, political science, and legal studies.

Suggested Citation

  • Harry Crane, 2015. "Clustering from Categorical Data Sequences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 810-823, June.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:510:p:810-823
    DOI: 10.1080/01621459.2014.983521
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    Cited by:

    1. Eric Golinko & Xingquan Zhu, 2019. "Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks," Information Systems Frontiers, Springer, vol. 21(1), pages 125-142, February.

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