Efficient discovery of interesting patterns based on strong closedness

Mario Boley, Tamás Horváth, Stefan Wrobel

Research output: Contribution to journalArticlepeer-review

Abstract

Finding patterns that are interesting to a user in a certain application context is one of the central goals of data mining research. Regarding all patterns above a certain frequency threshold as interesting is one way of defining interestingness. In this paper, however, we argue that in many applications, a different notion of interestingness is required in order to be able to capture " long" , and thus particularly informative, patterns that are correspondingly of low frequency. To identify such patterns, our proposed measure of interestingness is based on the degree or strength of closedness of the patterns. We show that (i) indeed this definition selects long interesting patterns that are difficult to identify with frequency-based approaches, and (ii) that it selects patterns that are robust against noise and/or dynamic changes. We prove that the family of interesting patterns proposed here forms a closure system and use the corresponding closure operator to design a mining algorithm listing these patterns in amortized quadratic time. In particular, for nonsparse datasets its time complexity is O(nm) per pattern, where n denotes the number of items and m the size of the database. This is equal to the best known time bound for listing ordinary closed frequent sets, which is a special case of our problem. We also report empirical results with real-world datasets.

Original languageEnglish
Pages (from-to)346-360
Number of pages15
JournalStatistical Analysis and Data Mining
Volume2
Issue number5-6
DOIs
StatePublished - 2009
Externally publishedYes

Keywords

  • Closure operators
  • Concept lattice pruning
  • Constrained-based pattern mining
  • Formal concept analysis
  • Frequent itemset mining
  • Interestingness notions
  • Polynomial delay listing algorithms

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

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