How to quickly find a witness

Daniel Kifer, Johannes Gehrke, Cristian Bucila, Walker White

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. We present a more unified view of mining constrained itemsets such that most existing algorithms can be easily extended to handle constraints for which they were not designed a-priori. We apply this technique to mining itemsets with restrictions on their variance - a problem that has been open for several years in the data mining community.

Original languageEnglish (US)
Title of host publicationConstraint-Based Mining and Inductive Databases - European Workshop on Inductive Databases and Constraint Based Mining, Revised Selected Papers
Pages216-242
Number of pages27
DOIs
StatePublished - Jun 23 2006
EventEuropean Workshop on Inductive Databases and Constraint Based Mining - Hinterzarten, Germany
Duration: Mar 11 2004Mar 13 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3848 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Workshop on Inductive Databases and Constraint Based Mining
CountryGermany
CityHinterzarten
Period3/11/043/13/04

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'How to quickly find a witness'. Together they form a unique fingerprint.

  • Cite this

    Kifer, D., Gehrke, J., Bucila, C., & White, W. (2006). How to quickly find a witness. In Constraint-Based Mining and Inductive Databases - European Workshop on Inductive Databases and Constraint Based Mining, Revised Selected Papers (pp. 216-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3848 LNAI). https://doi.org/10.1007/11615576_11