Apriori-based frequent itemset mining algorithms on MapReduce

Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12

Volumn , Issue , 2012, Pages

  Lin, Ming Yen ^a   Lee, Pei Yu ^a   Hsueh, Sue Chen ^b  

^a FENG CHIA UNIVERSITY   (Taiwan)

^b CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Association rule mining; Hadoop; MapReduce

Indexed keywords

APRIORI ALGORITHMS; CLUSTER SIZES; DATA SET SIZE; DATA SETS; FREQUENT ITEMSET MINING; HADOOP; HETEROGENEOUS CLUSTERS; MAP-REDUCE; PARALLELIZATIONS; SCALE-UP;

COMMUNICATION; DATA MINING; INFORMATION MANAGEMENT;

ALGORITHMS;

EID: 84860502035     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2184751.2184842     Document Type: Conference Paper

References (23)

1. A. Abouzeid, K. Bajda-Pawlikowski, D.J. Abadi, A. Rasin, and A. Silberschatz: HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. In: Proceedings of the VLDB Endowment (PVLDB), 2(1): 922-933, 2009
2. R. Agrawal and R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the Twentieth International Conference on Very Large Databases (VLDB), pp. 487-499, 1994
3. R. Agrawal and J.C. Shafer: Parallel Mning of Association Rules, In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 8(6): 962-969, 1996
4. S. Cong, J. Han, J. Hoeflinger, and D. Padua: A Sampling-based Framework for Parallel Data Mining. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 255-265, 2005 (Pubitemid 43182852)
5. J. Dean and S. Ghemawat: Mapreduce: Simplified Data Processing on Large Clusters. In: Proceedings of the Sixth Symposium on Operating System Design and Implementation (OSDI), pp. 137-150, 2004
6. J. Dean and S. Ghemawat: MapReduce: A Flexible Data Processing Tool. In: Communications of the ACM (CACM), 53(1):72-77, 2010
7. J. Han, J. Pei, and Y. Yin: Mning Frequent Patterns without Candidate Generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 29(2):1-12, 2000
8. J.-W. Huang, S.-C. Lin, and M.-S. Chen: DPSP: Distributed Progressive Sequential Pattern Mining on the Cloud. In: Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mning (PAKDD), pp. 27-34, 2010
9. D. Jiang, B.C. Ooi, L. Shi, and S. Wu: The Performance of MapReduce: An In-depth Study. In: Proceedings of the VLDB Endowment (PVLDB), 3(1): 472-483, 2010
10. J.L. Johnson: SQL in the Clouds. In: Computing in Science and Engineering, 11(4):12-28, 2009
11. H. Li, Y. Wang, D. Zhang, M. Zhang, and EY. Chang: PFP: Parallel FP-Growth for Query Recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 107-114, 2008
12. R. Li, L. Ju, Z. Peng, Z. Yu, and C. Wang: Batch Text Similarity Search with MapReduce. In: Proceedings of the 13th Asia-Pacific Web Conference on Web Technologies and Applications, pp. 412-423, 2011
13. R. McCreadie, C. Macdonald, and I. Ounis: MapReduce Indexing Strategies: Studying Scalability and Efficiency. In: Information Processing and Management, pp. 1-16, 2011
14. A. Mueller: Fast Sequential and Parallel Algorithms for Association Rule Mining: A Comparison. In: Tech. Report CS-TR-3515, University of Maryland, College Park, Md., 1995
15. A. Pavlo, E. Paulson, A. Rasin, D.J. Abadi, D.J. DeWitt, S. Madden, and M. Stonebraker: A Comparison of Approaches to Large Scale Data Analysis. In: Proceedings of SIGMOD, pp. 165-178, 2009
16. T. Shintani and M. Kitsuregawa: Hash Based Parallel Algorithms for Mining Association Rules. In: Proceedings of the Fourth International Conference on Parallel and Distributed Information Systems, pp. 19-31, 1996
17. K. Shvachko, H. Kuang, S. Radia, and R. Chansler: The Hadoop Distributed File System. In: Proceedings of the Mass Storage Systems and Technologies (MSST), pp. 1-10, 2010
18. M. Stonebraker, D.J. Abadi, D.J. DeWitt, S. Madden, E. Paulson, A. Pavlo, and A. Rasin: MapReduce and Parallel DBMSs: Friends or Foes? In: Communications of the ACM (CACM), 53(1):64-71, 2010
19. R. Vernica, M. J. Carey, C. Li: Efficient Parallel Set-Similarity Joins Using MapReduce. In: Proceedings of SIGMOD, pp. 495-506, 2010
20. K.-M. Yu, J. Zhou, T.-P. Hong, and J.-L. Zhou: A Load-Balanced Distributed Parallel Mining Algorithm, Expert Systems with Applications, 37(3):2459-2464, 2009
21. M. J. Zaki: Parallel and Distributed Association Mining: A Survey. In: IEEE Concurrency, 7(4):14-25, 1999
22. Z. Zheng, R. Kohavi, and L. Mason: Real world performance of association rule algorithms. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2001, pp. 401-406, 2001
23. Hadoop, http://hadoop.apache.org/(Accessed: 09/01/2011)