期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：9
出版社：S.S. Mishra
摘要：Data mining algorithms have been around for discovering knowledge from large real world data sets. Especially they operate on historical data in OLAP (Online Analytical Processing) applications. Frequent itemset mining is used in many applications such as query expansion, inductive databases, and association rule mining. When an itemset is repeated in specified number of times in given dataset, it is known as frequent itemset. Frequent itemset which does not appear in other freq uent itemset is known as maximal itemset. If the frequent itemset is not included in other itemset then it is known as closed itemset. These itemsets have their utility in data mining applications. Recently Uno et al. proposed algorithms to discover frequent itemsets, closed itemsets and maximal itemsets. In this paper we implement these algorithms. We also built a prototype application to demonstrate the proof of concept. The experimental results revea led that the application is useful and can be used in real world applications
