Formal concept sampling for counting and threshold-free local pattern mining

Mario Boley, Thomas Gärtner, Henrik Grosskreutz

Research output: Contribution to conferencePaperpeer-review

Abstract

We describe a Metropolis-Hastings algorithm for sampling formal concepts, i.e., closed (item-) sets, according to any desired strictly positive distribution. Important applications are (a) estimating the number of all formal concepts as well as (b) discovering any number of interesting, non-redundant, and representative local patterns. Setting (a) can be used for estimating the runtime of algorithms examining all formal concepts. An application of setting (b) is the construction of data mining systems that do not require any user-specified threshold like minimum frequency or confidence.

Original languageEnglish
Pages177-188
Number of pages12
DOIs
StatePublished - 2010
Externally publishedYes
Event10th SIAM International Conference on Data Mining, SDM 2010 - Columbus, OH, United States
Duration: 29 Apr 20101 May 2010

Conference

Conference10th SIAM International Conference on Data Mining, SDM 2010
Country/TerritoryUnited States
CityColumbus, OH
Period29/04/101/05/10

ASJC Scopus subject areas

  • Software

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