Gwangbum Pyun, Unil Yun, Journal of Internet Computing and Services, Vol. 14, No. 1, pp. 63-0, Feb. 2013
10.7472/jksii.2013.14.63, Full Text:  HTML
Keywords: approximate frequent pattern mining, Chernoff technique, probabilistic technique, Performance Evaluation, scalability

Abstract

Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.

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