Formal concept sampling for counting and threshold-free local pattern mining
Mario Boley, Thomas Gaertner and Henrik Grosskreutz
Tenth SIAM International Conference on Data Mining pp. 177-188, 2010.

Abstract

We describe a Metropolis-Hastings algorithm for sam- pling 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 in- teresting, non-redundant, and representative local pat- terns. Setting (a) can be used for estimating the runtime of algorithms examining all formal concepts. An appli- cation of setting (b) is the construction of data mining systems that do not require any user-specied threshold like minimum frequency or condence.

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