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

• Main Authors: LO, David, DING, Bolin, Lucia, -, Han, Jiawei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/1344; http://www.cs.uiuc.edu/homes/hanj/pdf/icde11_dlo.pdf
• Institution: Singapore Management University

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.