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Pattern Detection and Discovery pp 110–124Cite as

Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2447))

Abstract

Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. Querying these databases needs for primitives to: (1) select, manipulate and query data, (2) select, manipulate and query “interesting” patterns (i.e., those patterns that satisfy certain constraints), and (3) cross over patterns and data (e.g., selecting the data in which some patterns hold). Designing such query languages is a long-term goal and only preliminary approaches have been studied, mainly for the association rule mining task. Starting from a discussion on the MINE RULE operator, we identify several open issues for the design of inductive databases dedicated to these descriptive rules. These issues concern not only the offered primitives but also the availability of efficient evaluation schemes. We emphasize the need for primitives that work on more or less condensed representations for the frequent itemsets, e.g., the (frequent) β-free and closed itemsets. It is useful not only for optimizing single association rule mining queries but also for sophisticated post-processing and interactive rule mining.

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Author information

Authors and Affiliations

  1. Laboratoire d’Ingénierie des Systèmes d’Information, Institut National des Sciences Appliquées de Lyon, Bâtiment Blaise Pascal, F-69621, Villeurbanne cedex, France

    Baptiste Jeudy

Authors
  1. Baptiste Jeudy
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  2. Boulicaut Jean-François
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College of Science, Technology and Medicine, Huxley Building, 180 Queen’s Gate, SW7 2BZ, London, UK

    David J. Hand , Niall M. Adams  & Richard J. Bolton ,  & 

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Jeudy, B., Jean-François, B. (2002). Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_9

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  • DOI: https://doi.org/10.1007/3-540-45728-3_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44148-9

  • Online ISBN: 978-3-540-45728-2

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