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http://dx.doi.org/10.7583/JKGS.2018.18.2.121

Frequent Pattern Mining By using a Completeness for BigData  

Park, In-Kyu (Dept. of Game Software, College of Engineering Joongbu University)
Publication Information
Journal of Korea Game Society / v.18, no.2, 2018 , pp. 121-130 More about this Journal
Abstract
Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. It prerequisites that any interesting pattern should occupy a maximum portion of the transactions it appears. But in our real world scenarios the completeness of any pattern is more likely to become various in transactions. Hence, we should also consider the problem of finding the qualified patterns with the significant values of the weighted support by completeness in order to reduce the loss of information within any pattern in transaction. In these pattern recommendation applications, patterns with higher completeness may lead to higher recall while patterns with higher completeness may lead to higher recall while patterns with higher frequency lead to higher precision. In this paper, we propose a measure of weighted support and completeness and an algorithm WSCFPM(weigted support and completeness frequent pattern mining). Our algorithm handles the invalidation of the monotone or anti-monotone property which does not hold on completeness. Extensive performance analysis show that our algorithm is very efficient and scalable for word pattern mining.
Keywords
Frequent Pattern Mining; Weighted Support; Completeness; Anti-monotone Property;
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