Sequential Pattern Mining with Optimization Calling MapReduce Function on MapReduce Framework 


Vol. 18,  No. 2, pp. 81-88, Apr.  2011
10.3745/KIPSTD.2011.18.2.81


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  Abstract

Sequential pattern mining that determines frequent patterns appearing in a given set of sequences is an important data mining problem with broad applications. For example, sequential pattern mining can find the web access patterns, customer`s purchase patterns and DNA sequences related with specific disease. In this paper, we develop the sequential pattern mining algorithms using MapReduce framework. Our algorithms distribute input data to several machines and find frequent sequential patterns in parallel. With synthetic data sets, we did a comprehensive performance study with varying various parameters. Our experimental results show that linear speed up can be achieved through our algorithms with increasing the number of used machines.

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[IEEE Style]

J. H. Kim and K. S. Shim, "Sequential Pattern Mining with Optimization Calling MapReduce Function on MapReduce Framework," The KIPS Transactions:PartD, vol. 18, no. 2, pp. 81-88, 2011. DOI: 10.3745/KIPSTD.2011.18.2.81.

[ACM Style]

Jin Hyun Kim and Kyu Seok Shim. 2011. Sequential Pattern Mining with Optimization Calling MapReduce Function on MapReduce Framework. The KIPS Transactions:PartD, 18, 2, (2011), 81-88. DOI: 10.3745/KIPSTD.2011.18.2.81.

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