Hybrid ASP-Based Approach to Pattern Mining

  • Sergey Paramonov
  • Daria Stepanova
  • Pauli Miettinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10364)

Abstract

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset and sequence mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains both for itemset and sequence mining.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sergey Paramonov
    • 1
  • Daria Stepanova
    • 2
  • Pauli Miettinen
    • 2
  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium
  2. 2.Max Planck Institute of InformaticsSaarbrückenGermany

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