Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database
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 dom...
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| Main Authors: | , , , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2011
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| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/1344 http://www.cs.uiuc.edu/homes/hanj/pdf/icde11_dlo.pdf |
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| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | 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. |
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