Compression-based measures for mining interesting rules

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

An interestingness measure estimates the degree of interestingness of a discovered pattern and has been actively studied in the past two decades. Several pitfalls should be avoided in the study such as a use of many parameters and a lack of systematic evaluation in the presence of noise. Compression-based measures have advantages in this respect as they are typically parameter-free and robust to noise. In this paper, we present J-measure and a measure based on an extension of the Minimum Description Length Principle (MDLP) as compression-based measures for mining interesting rules.

Original languageEnglish
Title of host publicationNext-Generation Applied Intelligence - 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Proceedings
Pages741-746
Number of pages6
DOIs
Publication statusPublished - 2009
Event22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009 - Tainan, Taiwan, Province of China
Duration: Jun 24 2009Jun 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5579 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009
Country/TerritoryTaiwan, Province of China
CityTainan
Period6/24/096/27/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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