Mining interesting sequential patterns for intelligent systems

International Journal of Intelligent Systems

Volumn 20, Issue 1, 2005, Pages 73-87

  Yen, Show Jane ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; CUSTOMER SATISFACTION; DATA ACQUISITION; DATABASE SYSTEMS; INFORMATION ANALYSIS; INTELLIGENT AGENTS; SEQUENTIAL MACHINES;

MINIMUM FREQUENT SEQUENCE; MINING SEQUENTIAL PATTERNS; SEQUENTIAL PURCHASING; TRANSACTION DATABASE;

MINING;

EID: 11844277111     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.20054     Document Type: Article

References (22)

1. Agrawal R, Srikant R. Fast algorithm for mining association rules. In: Bocca JB, Jarke M, Zaniolo C, editors. Proc Int Conf on Very Large Data Bases, Santiago de Chile. San Francisco, CA: Morgan Kaufmann; 1994. pp 487-499.
2. Agrawal R, Srikant R. Mining sequential patterns: Generalizations and performance improvements. In: Apers P, Bouzeghoub M, Gardarin G, editors. Int Conf on Extending Database Technology (EDBT), Avignon, France. Heidelberg, Germany: Springer-Verlag; 1996. pp 3-17.
3. Bayardo RJ Jr, Agrawal R. Mining the most interesting rules. In: Proc 5th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM Press; 1999. pp 145-154.
4. Brin S, Motwani R, Ullman JD, Tsur S. Dynamic itemset counting and implication rules for market basket data. In: Proc ACM SIGMOD Int Conf on Management of Data. New York: ACM Press; 1997. pp 255-264.
5. Liu B, Hsu W, Ma Y. Mining association rules with multiple minimum supports. In ACM Conf on Knowledge Discovery in Data (KDD). New York: ACM Press; 1999. pp 337-341.
6. Liu J, Pan Y, Wang K, Han J. Mining frequent item sets by opportunistic projection. In: ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining; 2002. pp 229-238.
7. Yen SJ. Mining generalized multiple-level association rules. Lecture Notes in Artificial Intelligence: Principles of Data Mining and Knowledge Discovery, Vol 1910; 2000. pp 679-684.
8. Zaki MJ. SPADE: An efficient algorithm for mining frequent sequences. Mach Learn J 2001;42:31-60.
9. Agrawal R, Srikant R. Mining sequential patterns. In: Yu PS, Chen ALP, editors. Proc Int Conf on Data Engineering. IEEE Computer Society; 1995. pp 3-14.
10. Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE'01, Heidelberg, Germany. IEEE Computer Society; 2001. pp 215-224.
11. Yen SJ, Cho CW. An efficient approach to discovering sequential patterns from large databases. Lecture Notes in Artificial Intelligence: Principles of Data Mining and Knowledge Discovery, Vol 1910; 2000. pp 685-690.
12. Yen SJ, Chen ALP. A graph-based approach for discovering various types of association rules. IEEE Trans Knowl Data Eng 2001; 13:839-845.
13. Yen SJ, Cho CW. An efficient approach for updating sequential patterns using database segmentation. Int J Fuzzy Syst 2001;3(2):422-431.
14. Han J, Pel J, Yin Y. Mining frequent patterns without candidate generation. In: Chen W, Naughton JF, Bernstein PA, editors. SIGMOD'00, Dallas, TX. ACM Press; 2000. pp 1-12.
15. Han J, Pei J, Lu H, Nishio S, Tang S, Yang D. H-Mine: Hyper-structure mining of frequent patterns in large databases. In: Cereone N, Lin TY, Wu X, editors. Proc. 2001 IEEE Int Conf on Data Mining (ICDM'01), San Jose, CA, November 29-December 2, 2001. IEEE Computer Society; 2001.
16. Yen SJ, Chen ALP. An efficient approach to discovering knowledge from large databases. In: Proc Int Conf on Parallel and Distributed Information Systems. IEEE Computer Society; 1996. pp 8-18.
17. Han J, Pei J. Mining Frequent patterns by pattern-growth: Methodology and implications. In: ACM SIGKDD, December 2000. New York: ACM Press; 2000. pp 30-36.
18. Meo R, Psaila G, Ceri S. A new SQL-like operator for mining association rules. In: Vijayaraman TM, Buchmann AP, Mohan C, editors. Proc Int Conf on Very Large Data Bases. San Francisco, CA: Morgna Kaufmann; 1996. pp 122-133.
19. Yen SJ, Chen ALP. An efficient data mining technique for discovering interesting association rules. In: Hameurlain A, Tjoa AM, editors. Proc 8th Int Conf and Workshop on Database and Expert Systems Applications. Heidelberg, Germany: Springer-Verlag; 1997. pp 664-669.
20. Yen SJ, Lee YS. Mining interesting association rules: A data mining language. Lect Notes Artif Intell 2002;2336:172-176.
21. Yen SJ. An efficient approach for analyzing user behaviors in a web-training environment. Int J Distance Educ Technol 2003;1:55-71.
22. Yen SJ, Lee YS. An efficient data mining algorithm for discovering web access patterns. Lect Notes Comput Sci Web Technol Appl 2003;2642:187-192.