文章基本信息

标题：An Improved Frequent Itemset Generation Algorithm Based On Correspondence
本地全文：下载
作者：Ajay R Y ; Sharath Kumar A ; Preetham Kumar 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2012
卷号：2
期号：5
页码：253-258
DOI：10.5121/csit.2012.2522
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by the number of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scan algorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database.
关键词：Maximal Frequent Itemset; Support; Data Mining; Mapping.