Abstract
Apriori algorithm is one of the basic algorithms introduced to solve the problem of frequent itemset mining (FIM). Since there is a new generation of affordable computers with parallel processing capability and it is easier to set up computer clusters, we can develop more efficient parallel FIM algorithms for these new systems. This paper investigates the use of trie data structure in parallel execution of Apriori algorithm, the potential problems during implementation, performance comparison of several parallel implementations and in order to increase the efficiency, proposes a new way of message passing for parallel Apriori on a computer cluster with PVM.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB. September 12-15, Chile, pp. 487–499 (1994), ISBN 1-55860-153-8
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of the 2000 ACM SIGMOD Int. Conf. Management of Data, Dallas, TX, pp. 1–12 (2000)
Zaki, M.J.: Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390 (2000)
Bodon, F.: Surprising results of trie-based FIM algorithms, IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI 2004). In: Goethals, B., Zaki, M.J., Bayardo, R. (eds.) CEUR Workshop Proceedings, Brighton, UK, November 2004, vol. 90 (2004)
Bodon, F.: A fast Apriori implementation. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2003)
Ferenc Bodon, A.: Survey on Frequent Itemset Mining, Technical Report, Budapest University of Technology and Economic (2006)
Han, Karypis, K.: Scalable Parallel Data Mining for Association Rules. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 277–288. ACM Press, New York (1997)
Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Transactions on Knowledge and Data Eng. 8(6), 962–969 (1996)
Ye: A Parallel Apriori Algorithm for Frequent Itemsets Mining. In: SERA, pp. 87–4 (2006)
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Guner, L., Senkul, P. (2007). Frequent Itemset Minning with Trie Data Structure and Parallel Execution with PVM. In: Cappello, F., Herault, T., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2007. Lecture Notes in Computer Science, vol 4757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75416-9_40
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DOI: https://doi.org/10.1007/978-3-540-75416-9_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75415-2
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