Association rule mining in peer-to-peer systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 34, Issue 6, 2004, Pages 2426-2438

  Wolff, Ran ^a   Schuster, Assaf ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Anytime algorithms; Association rule mining; Data mining; Local algorithms; Peer to peer

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; DATA COMMUNICATION SYSTEMS; DISTRIBUTED COMPUTER SYSTEMS; DISTRIBUTED DATABASE SYSTEMS; NETWORK PROTOCOLS; PROBLEM SOLVING; TELECOMMUNICATION TRAFFIC;

ANYTIME ALGORITHMS; ASSOCIATION RULE MINING; LOCAL ALGORITHMS; PEER-TO-PEER SYSTEMS;

DATA MINING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPARATIVE STUDY; COMPUTER NETWORK; DATA BASE; EVALUATION; FACTUAL DATABASE; INFORMATION DISSEMINATION; INFORMATION RETRIEVAL; METHODOLOGY; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER COMMUNICATION NETWORKS; DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; INFORMATION DISSEMINATION; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED;

EID: 10044279537     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2004.836888     Document Type: Article

References (28)

1. R. Agrawal, T. Imielinski, and A. N. Swami, Mining association rules between sets of items in large databases, presented at ACM SIGMOD Int. Conf. Management Data. [Online]. Available: citeseer.nj.nec.com/ agrawal93mining.html
2. R. Agrawal and J. Shafer, "Parallel mining of association rules," IEEE Trans. Knowledge Data Engineering, vol. 8, pp. 962-969, Dec. 1996.
3. D. Cheung, J. Han, V. Ng, A. Fu, and Y. Fu, "A fast distributed algorithm for mining association rules," in Proc. Int. Conf. Parallel Distributed Information Systems, Miami Beach, FL, Dec. 1996, pp. 31-44.
4. M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "Parallel algorithms for discovery of association rules," Data Mining Knowl. Discovery, vol. 1, no. 4, pp. 343-373, 1997.
5. E.-H. S. Han, G. Karypis, and V. Kumar, "Scalable parallel data mining for association rules," IEEE Trans. Knowledge Data Engineering, vol. 12, pp. 352-377, May/June 2000.
6. J.-L. Lin and M. H. Dunham, Mining association rules: Anti-skew algorithms, presented at Proc. 14th Int. Conf. Data Engineering (ICDE). [Online]. Available: http://www.seas.smu.edu/jun/Papers/MINE/now.ps
7. A. Schuster, R. Wolff, and D. Trock, "A high-performance algorithm for distributed association rule mining," in Proc. IEEE Conf. Data Mining ICDM, Melbourne, FL, 2003.
8. V. S. Ananthanarayana, D. K. Subramanian, and M. N. Murty, Scalable, distributed and dynamic mining of association rules. presented at Proc. Int. Conf. High Performance Computing HiPC. [Online]. Available: http://www.hipc.org/website/hipc2000/listpaper.html
9. P. Iko and M. Kitsuregawa, "Parallel fp-growth on pc cluster," in Proc. 7th Pacific-Asia Conf. Knowledge Discovery Data Mining (PAKDD), 2003.
10. O. R. Zaiane, M. El-Hajj, and P. Lu, "Fast parallel association rules mining without candidacy generation," in Proc. IEEE Int. Conf. Data Mining (ICDM), 2001, pp. 665-668.
11. J. S. Park, M.-S. Chen, and P. S. Yu, "Efficient parallel data mining for association rules," in Proc. ACM Int. Conf. Information Knowledge Management, Baltimore, MD, Nov. 1995, pp. 31-36.
12. D. Cheung and Y. Xiao, "Effect of data skewness in parallel mining of association rules," in Proc. 12th Pacific-Asia Conf. Knowledge Discovery Data Mining, Apr. 1998, pp. 48-60.
13. The Condor Project [Online]. Available: http://www.cs.wisc.edu/condor/
14. Setiηome [Online]. Available: http://setiathome.ssl.berkeley.edu/
15. United Devices Inc. [Online]. Available: http://www.ud.com/home.htm
16. Entropia [Online]. Available: http://www.entropia.com
17. D. W.-L. Cheung, J. Han, V. Ng, and C. Y. Wong, "Maintenance of discovered association rules in large databases: An incremental updating technique," in Proc. 12th Int. Conf. Data Engineering, ICDE, 1996, pp. 106-114.
18. S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka, "An efficient algorithm for the incremental updation of association rules in large databases," in Proc. ACM SIGKDD Conf. Knowledge Discovery Data Mining, Newport Beach, CA, Aug. 1997, pp. 263-266.
19. S. Thomas and S. Chakravarthy, Incremental mining of constrained associations, presented at Int. Conf. High Performance Computing HiPC. [Online]. Available: citeseer.nj.nec.com/301407.html
20. M. Zhang, B. Kao, D. W.-L. Cheung, and C. L. Yip, "Efficient algorithms for incremental update of frequent sequences," in Proc. Pacific-Asia Conf. Knowledge Discovery Data Mining, 2002, pp. 186-197.
21. G. S. Manku and R. Motwani, "Approximate frequency counts over data streams," in Proc. Int. Conf. Very Large Data Bases (VLDB), Hong Kong, China, Aug. 2002.
22. S. Kutten and D. Peleg, Fault-local distributed mending, presented at ACM Symp. Principle Distributed Computing (PODC). [Online]. Available: citeseer.nj.nec.com/kutten94faultlocal.html
23. S. Kutten and B. Patt-Shamir, "Stabilizing time-adaptive protocols," Theor. Comput. Sci., vol. 220, no. 1, pp. 93-111, 1999.
24. R. D. Schlichting and F. B. Schneider, "Fail-stop processors: An approach to designing fault-tolerant computing systems," ACM Trans. Comput. Syst. (TOCS), vol. 1, no. 3, pp. 222-238, 1983.
25. A. Schuster and R. Wolff, "Communication-efficient distributed mining of association rules," in Proc. ACM SIGMOD Int. Conf. Management Data, Santa Barbara, CA, May 2001, pp. 473-484.
26. S. Brin, R. Motwani, J. Ullman, and S. Tsur, "Dynamic itemset counting and implication rules for market basket data," SIGMOD Rec., vol. 6, no. 2, pp. 255-264, June 1997.
27. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th Int. Conf. Very Large Databases (VLDB), Santiago, Chile, Sept. 1994, pp. 487-499.
28. P.-N. Tan, V. Kumar, and J. Srivastava, "Selecting the right objective measure for association analysis," Inform. Syst., vol. 29, no. 4, pp. 293-313, June 2004.