Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database
Conference Proceeding Article
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.
International Conference on Data Engineering (ICDE)
City or Country
LO, David; DING, Bolin; Lucia, -; and Han, Jiawei.
Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database. (2011). International Conference on Data Engineering (ICDE). 1043-1054. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1344