Efficient algorithms for incremental update of frequent sequences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2336, Issue , 2002, Pages 186-197

  Zhang, Minghua ^a   Kao, Ben ^a   Cheung, David ^a   Yip, Chi Lap ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Data mining; Incrementalu pdate; Sequence

Indexed keywords

DATABASE SYSTEMS;

FREQUENT SEQUENCES; INCREMENTAL ALGORITHM; INCREMENTAL UPDATES; INCREMENTALU PDATE; SEQUENCE;

DATA MINING;

EID: 84945302882     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47887-6_18     Document Type: Conference Paper

References (16)

1. Rakesh Agrawal and Ramakrishnan Srikant. Mining sequential patterns. In Proc. of the 11th Int’l Conference on Data Engineering, Taipei, Taiwan, March 1995.
2. Necip Fazil Ayan, Abdullah Uz Tansel, and Erol Arkun. An efficient algorithm to update large itemsets with early pruning. In Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA USA, August 1999.
3. D. W. Cheung, J. Han, V. Ng, and C. Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating techniques. In Proc. 12th IEEE International Conference on Data Engineering (ICDE), New Orleans, Louisiana, U.S.A, March 1996.
4. D. W. Cheung, S. D. Lee, and B. Kao. A generalin crementaltec hnique for maintaining discovered association rules. In Proc. International Conference On Database Systems For Advanced Applications (DASFAA), Melbourne, Australia, April1997.
5. D. W. Cheung, V. Ng, and B. W. Tam. Maintenance of discovered knowledge: A case in multi-level association rules. In Proc. Second International Conference on Knowledge Discovery and Data Mining (KDD), Portland, Oregon, August 1996.
6. http://www.almaden.ibm.com/cs/quest/.
7. S. D. Lee and D. W. Cheung. Maintenance of discovered association rules: When to update? In Proc. 1997 ACM-SIGMOD Workshop on Data Mining and Knowledge Discovery (DMKD), Tucson, Arizona, May 1997.
8. E. Omiecinski and A. Savasere. Efficient mining of association rules in large dynamic databases. In Proc. BNCOD’98, 1998.
9. S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas. Incrementalan d interactive sequence mining. In Proceedings of the 1999 ACM 8th International Conference on Information and Knowledge Management (CIKM’99), Kansas City, MO USA, November 1999.
10. Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu. Prefixspan: Mining sequential patterns by prefixprojected growth. In Proc. 17th IEEE International Conference on Data Engineering (ICDE), Heidelberg, Germany, April 2001.
11. T. Imielinski, R. Agrawal and A. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD International Conference on Management of Data, page 207, Washington, D.C., May 1993.
12. N. L. Sarda and N. V. Srinivas. An adaptive algorithm for incremental mining of association rules. In Proc. DEXA Workshop’98, 1998.
13. Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. of the 5th Conference on Extending Database Technology (EDBT), Avignion, France, March 1996.
14. S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka. An efficient algorithm for the incremental updation of association rules in large databases. In Proc. KDD’97, 1997.
15. Mohammed J Zaki. Efficient enumeration of frequent sequences. In Proceedings of the 1998 ACM 7th International Conference on Information and Knowledge Management(CIKM’98), Washington, United States, November 1998.
16. Minghua Zhang, Ben Kao, C.L. Yip, and David Cheung. A GSP-based efficient algorithm for mining frequent sequences. In Proc. of IC-AI’2001, Las Vegas, Nevada, USA, June 2001.