Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database

Publication Type

Conference Proceeding Article

Publication Date

4-2011

Abstract

We are interested in scalable mining of a nonredundant 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-ofthe- art approach in mining recurrent rules by up to two orders of magnitude.

Discipline

Software Engineering

Research Areas

Software Systems

Publication

International Conference on Data Engineering (ICDE)

First Page

1043

Last Page

1054

ISBN

9781424489589

Identifier

10.1109/ICDE.2011.5767848

Publisher

IEEE

City or Country

Hannover

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICDE.2011.5767848

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