Message-driven FP-growth

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 29-36

  Neerbek, Jan ^a  

^a ALEXANDRA INSTITUTE   (Denmark)

Author keywords

Apriori; Cloud; Communication; Data mining; Distributed computing; FP growth; Market basket analysis; Scaling

Indexed keywords

ALGORITHMIC SOLUTIONS; ALGORITHMIC VARIANTS; APRIORI; COMPUTATIONAL OVERHEADS; DECISION SUPPORTS; FP GROWTHS; FREQUENT ITEMSET MINING; FREQUENT PATTERNS GROWTH; ITEM SETS; MARKET BASKET ANALYSIS; MESSAGE-DRIVEN; MINING PROBLEMS; PROBLEM SIZE; SCALING; TRANSACTIONAL DATA; WORKER NODES;

ALGORITHMS; CLOUDS; COMMERCE; COMMUNICATION; DATA MINING; DATA STRUCTURES; DECISION SUPPORT SYSTEMS; DIAGNOSIS; DIGITAL STORAGE; DISTRIBUTED COMPUTER SYSTEMS;

SOFTWARE ARCHITECTURE;

EID: 84866843769     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2361999.2362005     Document Type: Conference Paper

References (14)

1. R. Agrawal and J. C. Shafer. Parallel mining of association rules: Design, implementation and experience. In IBM Research Report. IBM Research Division, San Jose, Ca 95120, 1996.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases, pages 487-499, 1994.
3. L. M. Aouad, N. an Le-khac, and T. M. Kechadi. Distributed frequent itemsets mining in heterogeneous platforms. Journal of Engineering, Computing and Architecture, 2007.
4. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia. Above the clouds: A berkeley view of cloud computing. In Technical Report. University of California, Berkeley, 2009.
5. F. Bodon. A fast apriori implementation. In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.
6. G. Buehrer, S. Parthasarathy, S. Tatikonda, T. Kurc, and J. Saltz. Toward terabyte pattern mining: An architecture-conscious solution. In Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming, pages 2-12, 2007.
7. S. Cong, J. Han, J. Hoeinger, and D. Padua. A sampling-based framework for parallel data mining. In PPoPP '05: Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, pages 255-265. ACM Press, New York, NY, USA, 2005.
8. T. Erl, D. Chou, J. deVadoss, N. Gandhi, H. Kommapalati, B. Loesgen, C. Shittko, H. Wilhelmsen, and M. Williams. SOA with .NET & Windows Azure. Prentice Hall, 2010.
9. W. Fang, K. K. Lau, M. Lu, X. Xiao, C. K. Lam, P. Y. Yang, B. He, Q. Luo, P. V. Sander, and K. Yang. Parallel data mining on graphics processors. In Technical Report. The Hong Kong University of Science and Technology, 2008.
10. E. Gabriel, G. E. Fagg, G. Bosilca, T. Angskun, J. J. Dongarra, J. M. Squyres, V. Sahay, P. Kambadur, B. Barrett, andrew Lumsdaine, R. H. Castain, D. J. Daniel, R. L. Graham, and T. S. Woodall. Open MPI: Goals, concept, and design of a next generation MPI implementation. In Proceedings of the 11th European PVM/MPI Users' Group Meeting, Budapest, Hungary, pages 97-104, 2004.
11. J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidata generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, Kluwer Academic Publishers, 8:53-87, 2004.
12. H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang. Pfp: Parallel fp-growth for query recommendation. In Proceedings of the 2008 ACM conference on Recommender systems. New York, NY, USA, ACM, pages 107-114, 2008.
13. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-mime: Hyper-structure miningof frequent patterns in large databases. In Proc. 2001 Int. Conf. Data Mining (ICDM'01). San Jose, CA, pages 441-448, 2001.
14. I. Pramudiono and M. Kitsuregawa. Parallel fp-growth on pc cluster. Lecture Notes in Computer Science (Springer Berlin / Heidelberg), 2637(570), 2003.