Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence DatabaseInternational Conference on Data Engineering (ICDE)
Publication TypeConference Proceeding Article
AbstractWe 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.
City or CountryHannover
Citation InformationDavid LO, Bolin DING, - Lucia and Jiawei Han. "Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database" International Conference on Data Engineering (ICDE) (2011) p. 1043 - 1054
Available at: http://works.bepress.com/david_lo/37/