Logics for Representing Data Mining Tasks in Inductive Databases

  • Hong-Cheu Liu
  • Millist Vincent
  • Jixue Liu
  • Jiuyong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8506)


We present a logical framework for querying inductive databases, which can accommodate a variety of data mining tasks, such as classification, clustering, finding frequent patterns and outliers detection. We also address the important issues of the expressive power of inductive query languages. We show that the proposed logic programming paradigm has equivalent expressive power to an algebra for data mining presented in the literature [1].


Data Mining Association Rule Logic Programming Frequent Itemsets Expressive Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hong-Cheu Liu
    • 1
  • Millist Vincent
    • 1
  • Jixue Liu
    • 1
  • Jiuyong Li
    • 1
  1. 1.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia

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