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

David Lo, Bolin Ding, Lucia, and Jiawei Han

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.

Details

Publication typeInproceedings
Published inProceedings of the 28th IEEE International Conference on Data Engineering (ICDE 2011)
Pages1043-1054
PublisherIEEE Computer Society
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