Incremental mining for temporal association rules for crime pattern discoveries

Conferences in Research and Practice in Information Technology Series

Volumn 63, Issue , 2007, Pages 123-132

  Ng, Vincent ^a   Chan, Stephen ^a   Lau, Derek ^a   Ying, Cheung Man ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Crime analysis; Incremental mining; Temporal association rules

Indexed keywords

HANDLING TIME; HONG-KONG; INCREMENTAL ALGORITHM; INCREMENTAL MINING; NEGATIVE BORDERS; NUMERICAL ATTRIBUTES; PATTERN DISCOVERY; REAL-LIFE DATABASE; SHIPPING COMPANIES; TEMPORAL ASSOCIATION RULE; TEMPORAL DATABASE;

ALGORITHMS; CRIME; DATABASE SYSTEMS; NUMERICAL METHODS; TAR;

ASSOCIATION RULES;

EID: 84872859190     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (13)

1. Agrawal, R., Imielinski, T. and Swami, A. (1993): Mining association rules between sets of items in large databases. Proc. ACM SIGMOD International Conference on Management of Data, Washington DC, USA, 22:207-216, ACM Press.
2. Agrawal. R., and Srikant. R. (1994) Fast algorithms for mining association rules. Proc. of the 20th Int'l Conf. on Very Large Database, Santiago, Chile, Sept. 1994, pp. 487-499.
3. Ayad. A., El-Makky. N., and Taha. Y. (2001) Incremental Mining of Constrained Association Rules. In First International SIAM Conference on Data Mining (SDM01), Chicago, April 2001. http://www.siam.org/meetings/sdm01/pdf/sdm01_01.pdf.
4. Ayan, N.F., Tansel, A.U., and Arkun, E. (1999) An efficient algorithm to update large itemsets with early pruning. The 5th Conference on Knowledge Discovery and Data Mining (SIGKDD99), AMC Press, San Diego, USA, pp. 439-450.
5. Cheng. H., Yan. X., and Han. J. (2004) "IncSpan: Incremental Mining of Sequential Patterns in LargeDatabase", Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004, pp. 527-532.
6. Chang, C.H., Yang S.H. (2003) Enhancing SWF for incremental association mining by itemset maintenance. The 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PKADD 203, LNAI 2637, Springer, Seoul, Korea, pp 301-312.
7. Cheung. D.W., Han. J., Ng. V., Fu. A., and Fu. Y. (1996) A fast distributed algorithm for mining association rules. In Proc. 1996 Int. Conf. Parallel and Distributed Information Systems, pp. 31-44, Miami Beach, FL. Dec 1996
8. Cheung. D.W., Han. J., Ng. V., and Wong. Y. (1996) Maintenance of discovered association rules in large databases: An incremental updating technique. In Proc. 1996 Int. Conf. Data Engineering (ICDE'96), pp. 106-114, New Orleans, LA, Feb. 1996
9. Cheung. D.W., Lee, S.D., Kao, B. (1997) A general incremental technique for maintaining discovered association rules. The 5th International Conference on Database Systems for Advanced Applications, Melbourne, Australia, pp. 185-194.
10. Toivonen, H. (1996) Sampling large database for association rules. In 22nd International Conference on Very Large Database (VLDB'96), pp. 135-145, Mumbay, India, September 1996.
11. Wang. W., Yang. Y., and Muntz. R. (1999) Temporal Association Rules with Numerical Attributes. NCLA CSD Technical Report 990011, 1999.
12. Wang. W., Yang. Y., and Muntz. R. (2001) TAR: temporal association rules on evolving numerical attributes,. Proceedings of the 17th IEEE International Conference on Data Engineering, pp. 283-292, 2001.
13. Zheng. Q., Xu. K., and Ma. S. (2002) The Algorithm of Updating Sequetial Pattern, 2002, http://xxx.arxiv.org/ftp/cs/papers/0203/0203027.pdf.