期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：9-3
出版社：Seventh Sense Research Group
摘要：Given a large database of customer transactions, where each transaction consists of cusid, trans time, and the items bought in the transaction. Introduce the problem of mining sequential patterns over such databases. In this paper, propose three algorithms LCM, LCMfreq, and LCMmax for data mining all maximal frequent sets, frequent sets, frequent closed itemsets, respectively from databases of transactions. The main theoretical contribution is that to construct tree shaped transversal routes composed of only frequent closed itemsets, which is induced by a parentchild relationship defined on frequent closed itemsets. By traversing the route in a depthfirst manner, LCM finds all frequent closed itemsets in polynomial time, without storing the previously obtained closed itemsets in memory. Introduces a several algorithmic techniques using the sparse and dense structures of input data and algorithms for enumerating all frequent itemsets and maximal frequent itemsets are obtained from LCM as its variants.
关键词：Itemsets; Association rule mining; LCM; Frequent items
