Bidirectional mining of non-redundant recurrent rules from a sequence database

David Lo, Bolin Ding, Lucia, Jiawei Han

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

Abstract

We are interested in scalable mining of a non-redundant set of significant recurrent rules from a sequence database. Recurrent rules have the form whenever a series of precedent events occurs, eventually a series of consequent events occurs. They are intuitive and characterize behaviors in many domains. An example is the domain of software specification, in which the rules capture a family of properties beneficial to program verification and bug detection. We enhance a past work on mining recurrent rules by Lo, Khoo, and Liu to perform mining more scalably. We propose a new set of pruning properties embedded in a new mining algorithm. Performance and case studies on benchmark synthetic and real datasets show that our approach is much more efficient and outperforms the state-of-the-art approach in mining recurrent rules by up to two orders of magnitude.

Original languageEnglish (US)
Title of host publication2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Pages1043-1054
Number of pages12
DOIs
StatePublished - 2011
Event2011 IEEE 27th International Conference on Data Engineering, ICDE 2011 - Hannover, Germany
Duration: Apr 11 2011Apr 16 2011

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
Country/TerritoryGermany
CityHannover
Period4/11/114/16/11

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Fingerprint

Dive into the research topics of 'Bidirectional mining of non-redundant recurrent rules from a sequence database'. Together they form a unique fingerprint.

Cite this