Non-redundant sequential rules-Theory and algorithm

作者: David Lo , Siau-Cheng Khoo , Limsoon Wong

DOI: 10.1016/J.IS.2009.01.002

关键词:

摘要: A sequential rule expresses a relationship between two series of events happening one after another. Sequential rules are potentially useful for analyzing data in format, ranging from purchase histories, network logs and program execution traces. In this work, we investigate propose syntactic characterization non-redundant set built upon past work on compact representative patterns. is redundant if it can be inferred another having the same support confidence. When using mined as composite filter, replacing full with subset does not impact accuracy filter. We consider several sets based composition various types pattern sets-generators, projected-database generators, closed patterns completeness tightness these sets. characterize tight complete by defining Furthermore, compressed spirit similar to how serve representation Lastly, an algorithm mine rules. performance study shows that proposed significantly improves both runtime compactness over mining

参考文章(29)
Ramakrishnan Srikant, Rakesh Agrawal, Fast algorithms for mining association rules very large data bases. pp. 580- 592 ,(1998)
David Lo, Siau-Cheng Khoo, Jinyan Li, Mining and Ranking Generators of Sequential Pattern siam international conference on data mining. pp. 553- 564 ,(2008)
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
David Lo, Siau-Cheng Khoo, Chao Liu, Efficient mining of recurrent rules from a sequence database database systems for advanced applications. pp. 67- 83 ,(2008) , 10.1007/978-3-540-78568-2_8
Mohammed Javeed Zaki, Ching-Jiu Hsiao, CHARM : An Efficient Algorithm for Closed Itemset Mining siam international conference on data mining. pp. 457- 473 ,(2002)
F. Masseglia, F. Cathala, P. Poncelet, The PSP approach for mining sequential patterns Principles of Data Mining and Knowledge Discovery. pp. 176- 184 ,(1998) , 10.1007/BFB0094818
Behzad Mortazavi-Asl, Umeshwar Dayal, Qiming Chen, Jiawei Han, Jian Pei, Meichun Hsu, Helen Pinto, PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth international conference on data engineering. pp. 215- 224 ,(2001)
Mohammed J. Zaki, SPADE: An Efficient Algorithm for Mining Frequent Sequences Machine Learning. ,vol. 42, pp. 31- 60 ,(2001) , 10.1023/A:1007652502315
Ramakrishnan Srikant, Rakesh Agrawal, Mining sequential patterns: Generalizations and performance improvements Advances in Database Technology — EDBT '96. pp. 1- 17 ,(1996) , 10.1007/BFB0014140
Jiawei Han, Ramin Afshar, Xifeng Yan, CloSpan: Mining Closed Sequential Patterns in Large Databases. siam international conference on data mining. pp. 166- 177 ,(2003)