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

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.