Efficient strategies for many-task frequent pattern mining in cloud computing environments

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2010, Pages 620-623

  Lin, Kawuu W ^a   Luo, Yu Chin ^a  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

Author keywords

Association rule mining; Cloud computing; Data mining; Frequent pattern mining

Indexed keywords

ASSOCIATION RULE MINING; CLOUD COMPUTING; COMPUTATION TIME; EFFICIENT STRATEGY; EMPIRICAL EVALUATIONS; EXCELLENT PERFORMANCE; EXECUTION TIME; FREQUENT PATTERN MINING; FREQUENT PATTERNS; GOOGLE SEARCH ENGINE; LARGE DATABASE; PARALLEL AND DISTRIBUTED ALGORITHMS; PARALLEL AND DISTRIBUTED COMPUTING; TRANSACTION DATABASE;

ASSOCIATION RULES; COMPUTER SYSTEMS; CYBERNETICS; DATABASE SYSTEMS; SEARCH ENGINES;

DATA MINING;

EID: 78751503885     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2010.5641816     Document Type: Conference Paper

References (13)

1. R. Agrawal and R. Srikant. Quest Synthetic Data Generator. IBM Almaden Research Center, San Jose, California, http://www.almaden.ibm.com/cs/quest/ syndata.html.
2. R. Agrawal, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD Intl. Conf Management Data, 1993, pp. 207-216.
3. R. Agrawal, John C. Shafer, "Parallel Mining of Association Rules", IEEE Transactions on knowledge and Data Engineering, December 1996, pp. 962-969.
4. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Generation of Frequent Itemsets, Journal of Parallel and Distributed Computing, Volume 61, No 3, 2001, pp. 350-371.
5. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns Without Candidate Generation. Proc. of ACM Int. Conf. on Management of Data (SIGMOD), 2000, pp. 1-12.
6. J.D. Holt, S.M. Chung, "Parallel mining of association rules from text databases on a cluster of workstations," Proceedings of 18th International Symposium on Parallel and Distributed Processing, 2004, pp. 86.
7. P. Iko and M. Kitsuregawa, "Shared Nothing Parallel Execution of FPgrowth." DBSJ Letters, Volume 2, No.1, 2003, pp. 43-46.
8. A. Javed, A. Khokhar, "Frequent Pattern Mining on Message Passing Multiprocessor Systems," Distributed and Parallel database, Volume 16, Issue 3, 2004, pp. 321-334.
9. K. W. Lin and Y. C. Luo, "A Fast Parallel Algorithm for Discovering Frequent Patterns," Proceedings of the 2009 IEEE International Conference on Granular Computing, 2009
10. Y. Qiu, Y. J. Lan and Q. S. Xie, "An improved algorithm of mining from FP- tree," Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004, pp. 26-29.
11. E.-H. S. Han, G. Karypis, and V. Kumar. Scalable parallel data mining for association rules. IEEE Transactions on Knowledge and Data Engineering, Volume 12, No 3, 2000, pp. 352-377.
12. J. Zhou, K.-M. Yu, "Tidset-based Parallel FP-tree Algorithm for the Frequent Pattern Mining Problem on PC Clusters," Lecture Notes in Computer Science 5036, 2008, pp. 18-28.
13. J. Zhou, K.-M. Yu, Balanced Tidset-based Parallel FP-tree Algorithm for the Frequent Pattern Mining on Grid System, Fourth International Conference on Semantics, Knowledge and Grid, 2008, pp.103-108.