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

Mario Boley, Thomas Gärtner, Henrik Grosskreutz

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

31 Citations (Scopus)


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
Title of host publicationProceedings of the 10th SIAM International Conference on Data Mining, SDM 2010
Number of pages12
Publication statusPublished - 1 Dec 2010
Externally publishedYes
EventSIAM International Conference on Data Mining 2010 - Columbus, United States of America
Duration: 29 Apr 20101 May 2010
Conference number: 10th


ConferenceSIAM International Conference on Data Mining 2010
Abbreviated titleSDM 2010
Country/TerritoryUnited States of America

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