期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：41
期号：1
页码：111-121
出版社：Journal of Theoretical and Applied
摘要：Computer systems are often used to store large amounts of data from which individual records must be retrieved according to some search criterion. Thus the efficient storage of data to facilitate fast searching is an important issue. Frequent pattern mining was first proposed by Agrawal et al. for market basket analysis in the form of association rule mining. It analyses customer buying behavior by finding associations between the different items that customers place in their shopping baskets. Researchers have proposed several algorithms for generating frequent itemsets. Frequent itemsets are found from the dataset through several searching algorithmic approaches. The novel bit search technique is implemented in the existing association rule mining algorithms. Frequent itemsets are generated with the help of apriori based bit search technique is known as Bit Stream Mask Search and eclat based bit search technique is branded as Sparse Bit Mask Search. These two algorithms are implemented in six datasets namely T10100K, T40I10100K, Pump, connect-4, mushroom and chess. These six datasets again run in AprioriTrie and FP-Growth algorithms. All the algorithms are executed in 5% to 25% support level and the results are compared. Efficiency is proved through performance analysis.
关键词：Association Rules; Frequent Itemset Mining; Bit Search; Bit Stream Mask Search; Sparse Bit Mask Search
