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Mining for classes and patterns in behavioural data

Author

Listed:
  • N M Adams

    (Imperial College of Science, Technology and Medicine)

  • D J Hand

    (Imperial College of Science, Technology and Medicine)

  • R J Till

    (Imperial College of Science, Technology and Medicine)

Abstract

In this paper we compare and contrast the new data mining activity of pattern search with more traditional cluster analysis methods of data mining, in the context of credit data. In particular, we examine a set of behavioural data from a large UK bank relating to the status of current accounts over a twelve month period. We show how conventional clustering approaches can be used, for example to define broad categories of behaviour, whereas pattern search can be used to find small groups of accounts that exhibit distinctive behaviour.

Suggested Citation

  • N M Adams & D J Hand & R J Till, 2001. "Mining for classes and patterns in behavioural data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1017-1024, September.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:9:d:10.1057_palgrave.jors.2601202
    DOI: 10.1057/palgrave.jors.2601202
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    Citations

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    Cited by:

    1. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    2. David L. Olson, 2007. "Data mining in business services," Service Business, Springer;Pan-Pacific Business Association, vol. 1(3), pages 181-193, September.
    3. Bolton, Richard J. & Hand, David J. & Crowder, Martin, 2004. "Significance tests for unsupervised pattern discovery in large continuous multivariate data sets," Computational Statistics & Data Analysis, Elsevier, vol. 46(1), pages 57-79, May.
    4. Maha Bakoben & Tony Bellotti & Niall Adams, 2017. "Identification of Credit Risk Based on Cluster Analysis of Account Behaviours," Papers 1706.07466, arXiv.org.

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