A novel frequent pattern mining algorithm for very large databases in cloud computing environments

Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011

Volumn , Issue , 2011, Pages 399-403

  Lin, Kawuu W ^a   Chen, Pei Ling ^a   Chang, Weng Long ^a  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

Author keywords

Clustering; Data Mining; Distributed Computing formatting; Frequent Pattern Mining

Indexed keywords

CLUSTERING; COMPUTING ENVIRONMENTS; DATABASE SIZE; EFFICIENT DATA STRUCTURES; EMPIRICAL EVALUATIONS; EXCELLENT PERFORMANCE; EXECUTION TIME; FP TREE; FREQUENT PATTERN MINING; MAIN MEMORY; MEMORY REQUIREMENTS; MINIMUM SUPPORT; SECONDARY MEMORIES; VERY LARGE DATABASE;

CLOUD COMPUTING; DATA MINING; DATA STRUCTURES; DATABASE SYSTEMS; DISTRIBUTED COMPUTER SYSTEMS; FORESTRY; GRANULAR COMPUTING; SCALABILITY;

COMPUTER SYSTEMS;

ALGORITHMS; COMPUTATION; DATA BASES; DATA PROCESSING; FORESTRY; PATTERN RECOGNITION;

EID: 84863414351     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GRC.2011.6122630     Document Type: Conference Paper

References (11)

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, T. Imielinski*, and A. Swami, 1993, "Mining association rules between sets of items in large databases", In Proc. of the 1993 ACM-SIGMOD Int. Conf. on management of data (SIGMOD'93), pp. 207-216.
3. R. Agrawal, R. Srikant, Mining Sequential Patterns, in: Proc. of the 11th Int'l Conf. on Data Engineering, 1995, pp. 3-14.
4. R. J. Bayardo, Jr., Brute-force mining of high-confidence classification rules. In Proceedings of the 3rd international conference on knowledge discovery and data mining (KDD'97), Newport Beach, California, USA.
5. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, 1996, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231.
6. G. Grahne and J. Zhu, 2003, "Efficiently Using Prefix-trees in Mining Frequent Itemsets", In Proc. of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.
7. J. Han, J. Pei, and Y. Yin, 2000, "Mining Frequent Patterns without Candidate Generation", In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.1-12.
8. A. Javed, and A. Khokhar, 2004, "Frequent Pattern Mining on Message Passing Multiprocessor Systems", Distributed and Parallel Databases, vol. 16, pp. 321-334.
9. K. W. Lin, Y.-C. Luo, 2009, "A Fast Parallel Algorithm for Discovering Frequent Patterns", GRC '09. IEEE Int. Conf. on Granular Computing, pp. 398-403.
10. J. Zhou and K.-M. Yu, 2008, "Tidset-based Parallel FP-tree Algorithm for the Frequent Pattern Mining Problem on PC Clusters", Lecture Notes in Computer Science 5036, pp. 18-28. (Pubitemid 351777245)
11. J. Zhou and K.-M. Yu, 2008, "Balanced Tidset-based Parallel FP-tree Algorithm for the Frequent Pattern Mining on Grid System", Fourth Int. Conf. on Semantics, Knowledge and Grid, pp. 103-108.