Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-78568-2_8
Title: Efficient mining of recurrent rules from a sequence database
Authors: Lo, D.
Khoo, S.-C. 
Liu, C.
Issue Date: 2008
Source: Lo, D.,Khoo, S.-C.,Liu, C. (2008). Efficient mining of recurrent rules from a sequence database. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4947 LNCS : 67-83. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-78568-2_8
Abstract: We study a novel problem of mining 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". Recurrent rules are intuitive and characterize behaviors in many domains. An example is in the domain of software specifications, in which the rules capture a family of program properties beneficial to program verification and bug detection. Recurrent rules generalize existing work on sequential and episode rules by considering repeated occurrences of premise and consequent events within a sequence and across multiple sequences, and by removing the "window" barrier. Bridging the gap between mined rules and program specifications, we formalize our rules in linear temporal logic. We introduce and apply a novel notion of rule redundancy to ensure efficient mining of a compact representative set of rules. Performance studies on benchmark datasets and a case study on an industrial system have been performed to show the scalability and utility of our approach. © 2008 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41450
ISBN: 3540785671
ISSN: 03029743
DOI: 10.1007/978-3-540-78568-2_8
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