文章基本信息

标题：A New Association Rule Mining Based on Frequent Item Set
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作者：Sanober Shaikh ; Madhuri Rao ; S. S. Mantha 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2011
卷号：1
期号：3
页码：81-95
DOI：10.5121/csit.2011.1308
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper a new mining algorithm is defined based on frequent item set. Apriori Algorithm scans the database every time when it finds the frequent item set so it is very time consuming and at each step it generates candidate item set. So for large databases it takes lots of space to store candidate item set. The defined algorithm scans the database at the start only once and then makes the undirected item set graph. From this graph by considering minimum support it finds the frequent item set and by considering the minimum confidence it generates the association rule. If database and minimum support is changed, the new algorithm finds the new frequent items by scanning undirected item set graph. That is why it's executing efficiency is improved distinctly compared to traditional algorithm.
关键词：Undirected Item set Graph; Trade List