Non-redundant sequential rules-Theory and algorithm

Information Systems

Volumn 34, Issue 4-5, 2009, Pages 438-453

  Lo, David ^a   Khoo, Siau Cheng ^b   Wong, Limsoon ^b  

^a SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Frequent pattern mining; Non redundant rules; Sequential pattern mining; Sequential rules; Theoretical data mining

Indexed keywords

FREQUENT PATTERN MINING; NON-REDUNDANT RULES; SEQUENTIAL PATTERN MINING; SEQUENTIAL RULES; THEORETICAL DATA MINING;

INFORMATION MANAGEMENT; MINING;

SEQUENTIAL SWITCHING;

EID: 67349284423     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2009.01.002     Document Type: Article

References (29)

1. Agrawal R., and Srikant R. Mining sequential patterns. Proceedings of IEEE International Conference on Data Engineering (1995) 3-14
2. Zaki M. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42 (2001) 31-60
3. Pei J., Han J., Mortazavi-Asl B., Chen Q., Dayal U., and Hsu M. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings of IEEE International Conference on Data Engineering (2001) 215-226
4. Han J., Wang J., Lu Y., and Tzvetkov P. Mining top-k frequent closed patterns without minimum support. Proceedings of IEEE International Conference on Data Mining (2002) 211-218
5. Ayres J., Gehrke J., Yiu T., and Flannick J. Sequential pattern mining using a bitmap representation. Proceedings of SIGKDD Conference on Knowledge Discovery and Data Mining (2002) 429-435
6. Yan X., Han J., and Afshar R. Clospan: mining closed sequential patterns in large datasets. Proceedings of SIAM International Conference on Data Mining (2003)
7. Wang J., and Han J. BIDE: efficient mining of frequent closed sequences. Proceedings of IEEE International Conference on Data Engineering (2004) 79-90
8. Spiliopoulou M. Managing interesting rules in sequence mining. Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery (1999) 554-560
9. Agrawal R., and Srikant R. Fast algorithms for mining association rules. Proceedings of International Conference on Very Large Data Bases (1994) 487-499
10. Zhang M., Hsu W., and Lee M.-L. Mining progressive confident rules. Proceedings of SIGKDD Conference on Knowledge Discovery and Data Mining (2006) 803-808
11. Yang J., Evans D., Bhardwaj D., Bhat T., and Das M. Perracotta: mining temporal API rules from imperfect traces. Proceedings of International Conference on Software Engineering (2006) 282-291
12. Lo D., and Khoo S.-C. SMArTIC: toward building an accurate, robust and scalable specification miner. Proceedings of SIGSOFT Symposium on the Foundations of Software Engineering (2006) 265-275
13. Lo D., Khoo S.-C., and Liu C. Efficient mining of recurrent rules from a sequence database. Proceedings of International Conference on Database Systems for Advanced Applications (2008) 67-83
14. Windows Driver Kit: driver development tools-SpinLock 〈http://msdn.microsoft.com/en-us/library/aa469109.aspx〉.
15. Masseglia F., Cathala F., and Poncelet P. The PSP approach for mining sequential patterns. Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery (1998) 176-184
16. Srikant R., and Agrawal R. Mining sequential patterns: generalizations and performance improvements. Proceedings of International Conference on Extending Database Technology (1996) 25-29
17. Han J., Pei J., Mortazavi-Asl B., Chen Q., Dayal U., and Hsu M. FreeSpan: frequent pattern-projected sequential pattern mining. Proceedings of SIGKDD Conference on Knowledge Discovery and Data Mining (2000) 355-359
18. Lo D., Khoo S.-C., and Li J. Mining and ranking generators of sequential patterns. Proceedings of SIAM International Conference on Data Mining (2008) 553-564
19. Gao C., Wang J., He Y., and Zhou L. Efficient mining of frequent sequence generators. Proceedings of International Conference on World Wide Web (2008) 1051-1052
20. Han J., and Kamber M. Data Mining Concepts and Techniques (2001), Morgan Kaufmann, Los Altos, CA
21. Workshop on Dynamic Analysis 〈http://www.cs.wisc.edu/woda-2008/〉. Last accessed 24 February 2009.
22. Hutchins M., Foster H., Goradia T., and Ostrand T. Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria. Proceedings of International Conference on Software Engineering (1994) 191-200
23. Cleve H., and Zeller A. Locating causes of program failures. Proceedings of International Conference on Software Engineering (2005) 342-351
24. N B., M S., Gaudel M.-C., and Gouraud S.-D. A machine learning approach for statistical software testing. Proceedings of International Joint Conferences on Artificial Intelligence (2007) 2274-2279
25. Li Z., and Zhou Y. PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code. Proceedings of Joint Meeting of European Software Engineering Conference and SIGSOFT Symposium on the Foundations of Software Engineering (2005) 306-315
26. Zaki M., and Hsiao C. CHARM: an efficient algorithm for closed itemset mining. Proceedings of SIAM International Conference on Data Mining (2002)
27. Zaki M. Mining non-redundant association rules. Data Mining and Knowledge Discovery 9 3 (2004) 223-248
28. Calders T., and Goethals B. Mining all non-derivable frequent itemsets. Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery (2002) 74-85
29. Baralis E., Chiusano S., and Dutto R. Applying sequential rules to protein localization prediction. Computer and Mathematics with Applications 55 5 (2008) 867-878