文章基本信息

标题：Modified Frequent Itemset Mining using Itemset Tidset pair
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作者：Dr. Jitendra Agrawal ; Dr. Shikha Agrawal
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2017
卷号：8
期号：1
页码：24-29
语种：English
出版社：Ayushmaan Technologies
摘要：Many algorithms have been proposed to mine all the Frequent Itemsets in a transaction database. Most of these algorithms were based on apriori-like candidate set generation-and-test approach to generate the association rules from the transactional database. The apriori-like candidate approach can suffer from two nontrivial costs: it needs to generate a huge number of candidate sets, and it may need to repeatedly scan the database and check a large set of candidates by pattern matching. This paper thus attempts to propose a new data-mining algorithm “Modified FIMIT” (MFMIT) for mining all the Frequent Itemsets in a transaction database using Vertical Transaction Database format. To improve the performance of the FIMIT in terms of time, MFIMIT uses sentinel arrangement on the cost of storage. The proposed MFIMIT is efficient and scalable over large databases, and is faster than the previously proposed methods.
关键词：Association Rule;Data Mining;Frequent Itemset;Vertical Database Format