文章基本信息

标题：Enhanced GenMax Algorithm for Data Mining
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作者：Saifullahi Aminu Bello ; Abubakar Ado ; Tong Yujun 等
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2014
卷号：3
期号：3
页码：10610
出版社：S&S Publications
摘要：Association rules mining is an important branch of data mining. Most of association rules mining algorithmsmake use of only one minimum support to mine items, which may be of different nature. Due to the difference innature of items, some items may appear less frequent and yet they are very important, and setting a high minimumsupport may neglect those items. And setting a lower minimum support may result in combinatorial explosion. Thisresult in what is termed as “rare item problem”. To address that, many algorithm where developed based on multipleminimum item support, where each item will have its minimum support. In this research paper, a faster algorithm isdesigned and analyzed and compared to the widely known enhanced Apriori algorithm. An experiment has beenconducted and the results showed that the new algorithm can mine out not only the association rules to meet thedemands of multiple minimum supports and but also mine out the rare but potentially profitable items’ associationrules, and is also proved to be faster than the conventional enhanced Apriori.
关键词：Data Mining; Association Rules; Multiple Minimum Item supports; Apriori; GenMax Algorithm