A taxonomy of sequential pattern mining algorithms

ACM Computing Surveys

Volumn 43, Issue 1, 2010, Pages

  Mabroukeh, Nizar R ^a   Ezeife, C I ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

Apriori property; Association rules; Data mining; Early pruning; Frequent patterns; Lattice theory; Lexicographic order; Pattern growth; Prediction; Recommender systems; Sequence mining; Sequential patterns; Tree projection; Web log; Web usage mining

Indexed keywords

HIERARCHICAL SYSTEMS; LATTICE THEORY; TAXONOMIES; WEBSITES;

APRIORI; APRIORI PROPERTY; EARLY PRUNING; FREQUENT PATTERN; LEXICOGRAPHIC ORDER; PATTERN GROWTH; PROPERTY; SEQUENCE MINING; SEQUENTIAL PATTERNS; TREE PROJECTIONS; WEB USAGE MINING; WEBLOGS;

DATA MINING;

EID: 77951168149     PISSN: 03600300     EISSN: 15577341     Source Type: Journal    
DOI: 10.1145/1824795.1824798     Document Type: Article

References (48)

1. AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD Conference on Management of Data. ACM, New York, 207-216.
2. AGRAWAL, R. AND SRIKANT, R. 1994. Fast algorithms for mining association rules. In Proceedings of the1994 International Conference on Very Large Data Bases (VLDB'94). 487-499.
3. AGRAWAL, R. AND SRIKANT, R. 1995. Mining sequential patterns. In Proceedings of the 11th Conference on Data Engineering (ICDE'95), 3-14.
4. ANTUNES, C. AND OLIVEIRA, A. L. 2004. Sequential pattern mining algorithms: Trade-offs between speed and memory. In Proceedings of the Workshop on Mining Graphs, Trees and Sequences (MGTSECML/ PKDD '04).
5. AYRES, J., FLANNICK, J.,GEHRKE, J., AND YIU, T. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 429-435.
6. BRIN, S.,MOTWANI, R., ULLMAN, J.D., AND TSUR, S. 1997. Dynamic itemset counting and implication rules for market basket analysis. In Proceedings of the ACM-SIGMOD International Conference on Management of Data (SIGMOD'97). ACM, New York, 255-264.
7. CHIU, D.-Y., WU, Y.-H., AND CHEN, A. L. P. 2004. An efficient algorithm for mining frequent sequences by a new strategy without support counting. In Proceedings of the 20th International Conference on Data Engineering. 375-386.
8. DAVE, B. A. AND PRIESTLEY, H. A. 1990. Introduction to Lattices and Order. Cambridge University Press.
9. DUNHAM, M. H. 2003. Data Mining: Introductory and Advanced Topics. Prentice Hall, Englewood Cliffs, NJ.
10. EL-SAYED, M., RUIZ, C., AND RUNDENSTEINER, E. A. 2004. FS-Miner: Efficient and incremental mining of frequent sequence patterns in web logs. In Proceedings of the 6th Annual ACM International Workshop on Web Information and Data Management. ACM, New York, 128-135.
11. EZEIFE, C. I. AND LU, Y. 2005. Mining web log sequential patterns with position coded pre-order linked WAP-tree. Int. J. Data Mining Knowl. Discovery 10, 5-38.
12. EZEIFE, C. I., LU, Y., AND LIU, Y. 2005. PLWAP sequential mining: Open source code. In Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementation (SIGKDD), ACM, New York, 26-35.
13. FACCA, F. M. AND LANZI, P. L. 2003. Recent developments in web usage mining research. In Proceedings of the 5th International Conference on Data Warehousing and Knowledge Discovery. (DaWaK'03), Lecture Notes in Computer Science, Springer, Berlin.
14. FACCA, F. M. AND LANZI, P. L. 2005. Mining interesting knowledge from weblogs: A survey. Data Knowl. Eng. 53, 3 225-241.
15. GOETHALS, B. 2005. Frequent set mining. In The Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach Eds., Springer, Berlin, 377-397.
16. HAN, J. AND FU, Y. 1995. Discovery of multiple-level association rules from large databases. In Proceedings of the International Conference on Very Large Data Bases (VLDB'95). 420-431.
17. HAN, J., PEI, J.,MORTAZAVI-ASL, B.,CHEN, Q.,DAYAL, U., AND HSU,M.-C. 2000. Freespan: Frequent patternprojected sequential pattern mining. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, 355-359.
18. HAN, J., PEI, J., AND YIN, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'00), ACM, New York, 1-12.
19. HUANG, J.-W., TSENG, C.-Y., OU, J.-C., AND CHEN, M.-S. 2006. On progressive sequential pattern mining. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management. ACM, New York, 850-851.
20. IVÁNCSY, R. AND VAJK, I. 2006. Frequent pattern mining in web log data. Acta Polytech. Hungarica 3, 1, 77-90.
21. JIN, X. 2006. Task-oriented modeling for the discovery of web user navigational patterns. Ph.D. dissertation, School of Computer Science. DePaul University, Chicago, IL.
22. LIU, B. 2007. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Berlin.
23. LU, Y. AND EZEIFE, C. I. 2003. Position coded pre-order linked WAP-tree for web log sequential pattern mining. In Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, Springer, Berlin, 337-349.
24. MASSEGLIA, F., PONCELET, O., AND CICCHETTI, R. 1999. An efficient algorithm for web usage mining. Network Inform. Syst. J. 2, 571-603.
25. MASSEGLIA, F., TEISSEIRE, M., AND PONCELET, P. 2005. Sequential pattern mining: A survey on issues and approaches. In Encyclopedia of Data Warehousing and Mining,1-14.
26. NANDI, A. AND JAGADISH, H.V. 2007. Effective phrase prediction. In Proceedings of the International Conference on Very Large Data Bases (VLDB'07). 219-230.
27. NANOPOULOS, A. AND MANOLOPOULOS, Y. 2000. Finding generalized path patterns for web log data mining. In Proceedings of the East-European Conference on Advances in Databases and Information Systems. (Held jointly with the International Conference on Database Systems for Advanced Applications: Current Issues in Databases and Information Systems, 215-228.
28. PARK, J. S., CHEN, M. S., AND YU, P. S. 1995. An effective hash-based algorithm for mining association rules. In Proceedings of the 1995 ACM-SIGMOD International Conference on Management of Data (SIGMOD' 95). ACM, New York, 175-186.
29. PARTHASARATHY, S., ZAKI, M.J., OGIHARA, M., AND DWARKADAS, S. 1999. Incremental and interactive sequence mining. In Proceedings of the 8th International Conference on Information and Knowledge Management. ACM, New York, 251-258.
30. PEI, J.,HAN, J.,MORTAZAVI-ASL, B., AND PINTO, H. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the International Conference on Data Engineering. 215-224.
31. PEI, J.,HAN, J.,MORTAZAVI-ASL, B., AND ZHU, H. 2000. Mining access patterns efficiently from web logs. In Knowledge Discovery and Data Mining. Current Issues and New Applications. Lecture Notes Computer Science, vol. 1805, Springer, Berlin, 396-407.
32. RYMON, R. 1992. Search through systematic set enumeration. In Proceedings of the 3rd International Conference. on the Principles of Knowledge Representation and Reasoning. 539-550.
33. SAVASERE, A., OMIECINSKI, E., AND NAVATHE, S. 1995. An efficient algorithm for mining association rules in large databases. In Proceedings of the International Conference on Very Large Data Bases (VLDB'95). 432-443.
34. SONG, S., HU, H., AND JIN, S. 2005. HVSM: A new sequential pattern mining algorithm using bitmap representation. In Advanced Data Mining and Applications. Lecture Notes in Computer Science, vol. 3584, Springer, Berlin, 455-463.
35. SRIKANT, R. AND AGRAWAL, R. 1996. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology. Leture Notes in Computer Science, vol. 1057, Springer, Berlin, 3-17.
36. SRIVASTAVA, J., COOLEY, R., DESHPANDE, M., AND TAN, P.-N. 2000. Web usage mining: Discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations Newsl. 1, 2, 12-23.
37. TANASA, D. 2005. Web usage mining: contributions to intersites logs preprocessing and sequential pattern extraction with low support. Ph.D. dissertation, Université De Nice Sophia-Antipolis.
38. TOIVONEN, H. 1996. Sampling large databases for association rules. In Proceedings of the International Conference on Very Large Data Bases (VLDB'95). 134-145.
39. Wang, J. and Han, J. 2004. BIDE: Efficient mining of frequent closed sequences. In Proceedings of the 20th International Conference on Data Engineering. 79-90.
40. YANG, Z. AND KITSUREGAWA, M. 2005. LAPIN-SPAM: An improved algorithm for mining sequential pattern. In Proceedings of the 21st International Conference on Data Engineering Workshops. 1222.
41. YANG, Z., WANG, Y., AND KITSUREGAWA, M. 2005. LAPIN: Effective sequential pattern mining algorithms by last position induction. Tech. rep., Tokyo University. http://www.tkl.iis.utokyo. ac.jp/~yangzl/Document/LAPIN.pdf.
42. YANG, Z.,WANG, Y., AND KITSUREGAWA, M. 2006. An effective system for mining web log. In Proceedings of the 8th Asia-Pacific Web Conference (APWeb'06), 40-52.
43. YANG, Z., WANG, Y., AND KITSUREGAWA, M. 2007. LAPIN: Effective sequential pattern mining algorithms by last position induction for dense databases. In Advances in Databases: Concepts, Systems and Applications. Lecture Notes in Computer Science, vol. 4443, 1020-1023.
44. ZAKI, M. J. 1998. Efficient enumeration of frequent sequences. In Proceedings of the 7th International Conference on Information and Knowledge Management. 68-75.
45. ZAKI, M. J. 2000. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12, 3, 372-390.
46. ZAKI, M. J. 2001. SPADE: An efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31-60.
47. ZHENG, T. 2004. WebFrame: In pursuit of computationally and cognitively efficient web mining. Ph.D. dissertation, Department of Computing Science. University of Alberta, Edmonton.
48. ZILL, D. J. 1998. Calculus with Analytic Geometry 2nd ed. PWS-KENT.