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 | 10th SIAM International Conference on Data Mining, SDM 2010 - Columbus, OH, United States of America Duration: 29 Apr 2010 → 1 May 2010 |
Conference
| Conference | 10th SIAM International Conference on Data Mining, SDM 2010 |
|---|---|
| Country | United States of America |
| City | Columbus, OH |
| Period | 29/04/10 → 1/05/10 |
Cite this
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Formal concept sampling for counting and threshold-free local pattern mining. / Boley, Mario; Gärtner, Thomas; Grosskreutz, Henrik.
Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010. 2010. p. 177-188.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review
TY - GEN
T1 - Formal concept sampling for counting and threshold-free local pattern mining
AU - Boley, Mario
AU - Gärtner, Thomas
AU - Grosskreutz, Henrik
PY - 2010/12/1
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
ER -