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http://dx.doi.org/10.9728/dcs.2017.18.8.1593

Efficient Association Rule Mining based SON Algorithm for a Bigdata Platform  

Nguyen, Giang-Truong (Department of Electronics and Computer Engineering, Chonnam National University)
Nguyen, Van-Quyet (Department of Electronics and Computer Engineering, Chonnam National University)
Nguyen, Sinh-Ngoc (Department of Electronics and Computer Engineering, Chonnam National University)
Kim, Kyungbaek (Department of Electronics and Computer Engineering, Chonnam National University)
Publication Information
Journal of Digital Contents Society / v.18, no.8, 2017 , pp. 1593-1601 More about this Journal
Abstract
In a big data platform, association rule mining applications could bring some benefits. For instance, in a agricultural big data platform, the association rule mining application could recommend specific products for farmers to grow, which could increase income. The key process of the association rule mining is the frequent itemsets mining, which finds sets of products accompanying together frequently. Former researches about this issue, e.g. Apriori, are not satisfying enough because huge possible sets can cause memory to be overloaded. In order to deal with it, SON algorithm has been proposed, which divides the considered set into many smaller ones and handles them sequently. But in a single machine, SON algorithm cause heavy time consuming. In this paper, we present a method to find association rules in our Hadoop based big data platform, by parallelling SON algorithm. The entire process of association rule mining including pre-processing, SON algorithm based frequent itemset mining, and association rule finding is implemented on Hadoop based big data platform. Through the experiment with real dataset, it is conformed that the proposed method outperforms a brute force method.
Keywords
Big data Platform; Association Rule Mining; Frequent Itemsets; SON Algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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