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

Mario Boley; Thomas Gärtner; Henrik Grosskreutz

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

Mario Boley, Thomas Gärtner, Henrik Grosskreutz

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-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 language	English
Title of host publication	Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010
Pages	177-188
Number of pages	12
Publication status	Published - 1 Dec 2010
Externally published	Yes
Event	SIAM International Conference on Data Mining 2010 - Columbus, United States of America Duration: 29 Apr 2010 → 1 May 2010 Conference number: 10th

Conference

Conference	SIAM International Conference on Data Mining 2010
Abbreviated title	SDM 2010
Country/Territory	United States of America
City	Columbus
Period	29/04/10 → 1/05/10

Cite this

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title = "Formal concept sampling for counting and threshold-free local pattern mining",

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.",

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AU - Boley, Mario

AU - Gärtner, Thomas

AU - Grosskreutz, Henrik

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Y1 - 2010/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80052655684&partnerID=8YFLogxK

M3 - Conference Paper

AN - SCOPUS:80052655684

SP - 177

EP - 188

BT - Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

T2 - SIAM International Conference on Data Mining 2010

Y2 - 29 April 2010 through 1 May 2010

ER -

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

Abstract

Conference

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Cite this