Quantitative evaluation of approximate frequent pattern mining algorithms

Rohit Gupta, Gang Fang, Blayne Field, Michael Steinbach, Vipin Kumar

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

47 Scopus citations

Abstract

Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life datasets, this limits the recovery of frequent itemset patterns as they are fragmented due to random noise and other errors in the data. Hence, a number of methods have been proposed recently to discover approximate frequent itemsets in the presence of noise. These algorithms use a relaxed definition of support and additional parameters, such as row and column error thresholds to allow some degree of "error" in the discovered patterns. Though these algorithms have been shown to be successful in finding the approximate frequent itemsets, a systematic and quantitative approach to evaluate them has been lacking. In this paper, we propose a comprehensive evaluation framework to compare different approximate frequent pattern mining algorithms. The key idea is to select the optimal parameters for each algorithm on a given dataset and use the itemsets generated with these optimal parameters in order to compare different algorithms. We also propose simple variations of some of the existing algorithms by introducing an additional post-processing step. Subsequently, we have applied our proposed evaluation framework to a wide variety of synthetic datasets with varying amounts of noise and a real dataset to compare existing and our proposed variations of the approximate pattern mining algorithms. Source code and the datasets used in this study are made publicly available.

Original languageEnglish (US)
Title of host publicationKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Pages301-309
Number of pages9
DOIs
StatePublished - 2008
Event14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/24/088/27/08

Keywords

  • Approximate frequent itemsets
  • Association analysis
  • Error tolerance
  • Quantitative evaluation

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