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Detecting Anomalous and Exceptional Behaviour on Credit Data by Means of Association Rules

  • Miguel Delgado
  • Maria J. Martin-Bautista
  • M. Dolores Ruiz
  • Daniel Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)

Abstract

Association rules is a data mining technique for extracting useful knowledge from databases. Recently some approaches has been developed for mining novel kinds of useful information, such us peculiarities, infrequent rules, exception or anomalous rules. The common feature of these proposals is the low support of such type of rules. Therefore, finding efficient algorithms for extracting them are needed.

The aim of this paper is three fold. First, it reviews a previous formulation for exception and anomalous rules, focusing on its semantics and definition. Second, we propose efficient algorithms for mining such type of rules. Third, we apply them to the case of detecting anomalous and exceptional behaviours on credit data.

Keywords

Data mining association rules exception rules anomalous rules fraud credit 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miguel Delgado
    • 1
  • Maria J. Martin-Bautista
    • 1
  • M. Dolores Ruiz
    • 1
  • Daniel Sánchez
    • 1
    • 2
  1. 1.Dpt. Computer Science and A.I., CITIC-UGRUniversity of GranadaGranadaSpain
  2. 2.European Centre for Soft ComputingMieresSpain

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