Distributed mining of constrained patterns from wireless sensor data

Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)

Volumn , Issue , 2006, Pages 248-251

  Leung, Carson Kai Sang ^a   Khan, Quamrul I ^a   Hao, Boyu ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; DISTRIBUTED DATABASE SYSTEMS; EMBEDDED SYSTEMS; TREES (MATHEMATICS); USER INTERFACES;

CONSTRAINED PATTERNS; DISTRIBUTED MINING; ENVIRONMENTAL MONITORING; WIRELESS SENSOR DATA;

WIRELESS SENSOR NETWORKS;

EID: 34250773917     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IATW.2006.59     Document Type: Conference Paper

References (20)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. VLDB 1994, pp. 487-499.
2. R. Agrawal and J. Shafer. Parallel mining of association rules. IEEE TKDE, 8(6), Dec. 1996, pp. 962-969.
3. R.J. Bayardo. Efficiently mining long patterns from databases. In Proc. SIGMOD 1998, pp. 85-93.
4. F. Bonchi and C. Lucchese. On closed constrained frequent pattern mining. In Proc. ICDM 2004, pp. 35-42.
5. S. Brin et al. Beyond market baskets: generalizing association rules to correlations. In Proc. SIGMOD 1997, pp. 265-276.
6. C. Bucila et al. DualMiner: a dual-pruning algorithm for itemsets with constraints. In Proc. SIGKDD 2002, pp. 42-51.
7. D.W. Cheung et al. A fast distributed algorithm for mining association rules. In Proc. PDIS 1996, pp. 31-42.
8. W. Cheung and O.R. Zaïane. Incremental mining of frequent patterns without candidate generation or support constraint. In Proc. IDEAS 2003, pp. 111-116.
9. F. Coene et al. T-trees, vertical partitioning and distributed association rule mining. In Proc. ICDM 2003, pp. 513-516.
10. M. El-Hajj and O.R. Zaïane. Parallel leap: large-scale maximal pattern mining in a distributed environment. In Proc. ICPADS 2006, pp. 135-142.
11. K. Gade et al. Efficient closed pattern mining in the presence of tough block constraints. In Proc. SIGKDD 2004, pp. 138-147.
12. J. Han et al. Mining frequent patterns without candidate generation. In Proc. SIGMOD 2000, pp. 1-12.
13. L.V.S. Lakshmanan, C.K.-S. Leung, and R.T. Ng. Efficient dynamic mining of constrained frequent sets. ACM TODS, 28(4), pp. 337-389, Dec. 2003.
14. C.K.-S. Leung et al. CanTree: a tree structure for efficient incremental mining of frequent patterns. In Proc. ICDM 2005, pp. 274-281.
15. C.K.-S. Leung et al. OSSM: a segmentation approach to optimize frequency counting. In Proc. ICDE 2002, pp. 583-592.
16. R.T. Ng et al. Exploratory mining and pruning optimizations of constrained associations Rules. In Proc. SIGMOD 1998, pp. 13-24.
17. J.S. Park et al. Using a hash-based method with transaction trimming for mining association rules. IEEE TKDE, 9(5), pp. 813-825, Sept./Oct. 1997.
18. J. Pei et al. Mining frequent itemsets with convertible constraints. In Proc. ICDE 2001, pp. 433-442.
19. A. Schuster et al. A high-performance distributed algorithm for mining association rules. KAIS, 7(4), May 2005, pp. 458-475.
20. M.J. Zaki. Parallel and distributed association mining: a survey. IEEE Concurrency, 7(4), Oct.-Dec. 1999, pp. 14-25.