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Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data

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  • Carem C. Fabris

    (CPGEI, CEFET-PR, Brazil)

  • Alex A. Freitas

    (University of Kent, UK)

Abstract

This paper focuses on the discovery of surprising unexpected patterns based on a data mining method that consists of detecting instances of Simpson’s paradox. By its very nature, instances of this paradox tend to be surprising to the user. Previous work in the literature has proposed an algorithm for discovering instances of that paradox, but it addressed only flat data stored in a single relation. This work proposes a novel algorithm that considerably extends that previous work by discovering instances of Simpson’s paradox in hierarchical multidimensional data — the kind of data typically found in data warehouse and OLAP environments. Hence, the proposed algorithm can be regarded as integrating the areas of data mining and data warehousing by using an adapted data mining technique to discover surprising patterns from data warehouse and OLAP environments.

Suggested Citation

  • Carem C. Fabris & Alex A. Freitas, 2006. "Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 2(1), pages 27-49, January.
    • Handle: RePEc:igg:jdwm00:v:2:y:2006:i:1:p:27-49
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