Database Reference
In-Depth Information
6
Conclusions and Summary
This chapter presents a comprehensive survey and analysis of main approaches to
sequential pattern mining. Two main classes of algorithms (i. e., Apriori-based ap-
proaches and pattern growth algorithms) for sequential pattern mining are discussed
in detail. Additionally, various kinds of extensions of sequential pattern mining
are also covered in this chapter, including closed, multi-level, multi-dimensional,
top- k closed sequential pattern, frequent episode mining and incremental, hybrid,
approximate methods.
References
1. J. Wang, “Sequential patterns,” Encyclopedia of Database Systems. LING LIU and M. TAMER
OZSU (Eds.) , pp. 2621-2626, 2009.
2. J. Pei, J. Han, and W. Wang, “Constraint-based sequential pattern mining: the pattern-growth
methods,” J. Intell. Inf. Syst. , vol. 28, no. 2, pp. 133-160, Apr. 2007.
3. J. Han, J. Pei, and X. Yan, “Sequential pattern mining by pattern-growth: Principles and
extensions,” StudFuzz , vol. 180, pp. 183-220, 2005.
4. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future
directions,” Data Min. Knowl. Discov. , vol. 15, no. 1, pp. 55-86, Aug. 2007.
5. N. R. Mabroukeh and C. I. Ezeife, “A taxonomy of sequential pattern mining algorithms,”
ACM Comput. Surv. , vol. 43, no. 1, pp. 3:1-3:41, Dec. 2010.
6. C. H. Mooney and J. F. Roddick, “Sequential pattern mining—approaches and algorithms,”
ACM Comput. Surv. , vol. 45, no. 2, pp. 19:1-19:39, Mar. 2013.
7. R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in
large databases,” in ACM SIGMOD conference , 1993, pp. 207-216.
8. R. Agrawal and R. Srikant, “Mining sequential patterns,” in ICDE Conference , 1995, pp. 3-14.
9. R. Srikant and R. Agrawal, “Mining sequential patterns: Generalizations and performance
improvements,” in EDBT Conference , 1996, pp. 3-17.
10. F. Masseglia, F. Cathala, and P. Poncelet, “The psp approach for mining sequential patterns,”
in PKDD Conference , 1998, pp. 176-184.
11. M. J. Zaki, “Spade: An efficient algorithm for mining frequent sequences,” Mach. Learn. ,
vol. 42, no. 1-2, pp. 31-60, Jan. 2001.
12. J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, “Sequential pattern mining using a bitmap
representation,” in ACM SIGKDD Conference , 2002, pp. 429-435.
13. L. Savary and K. Zeitouni, “Indexed bit map (ibm) for mining frequent sequences,” in PKDD
Conference , 2005, pp. 659-666.
14. Z. Yang and M. Kitsuregawa, “Lapin-spam: An improved algorithm for mining sequential
pattern,” in ICDE Workshops , 2005.
15. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu, “Freespan: frequent
pattern-projected sequential pattern mining,” in ACM SIGKDD Conference , 2000, pp. 355-359.
16. J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen, U. Dayal, and M. chun Hsu, “Prefixspan:
Mining sequential patterns efficiently by prefix-projected pattern growth,” in ICDE Conference ,
2001, pp. 215-224.
17. J. Han and J. Pei, “Mining frequent patterns by pattern-growth: methodology and implications,”
SIGKDD Explor. Newsl. , vol. 2, no. 2, pp. 14-20, Dec. 2000.
18. C. Raïssi and J. Pei, “Towards bounding sequential patterns,” in ACM SIGKDD , 2011, pp. 1379-
1387.
19. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,”
in VLDB Conference , 1994, pp. 487-499.
Search WWH ::




Custom Search