Research On Algorithms Of Mining Association Rules

Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data. It is a new subject that involves a lot of subjects and develops with these subjects. Association rule mining is an important branch of data mining to discover previously unknown, interesting relationships among attributes from large databases.This paper analyses some of the existed algorithms of mining association rules, and proposes an new algorithm FFC based on the algorithm AprioriTid and Close which is used to mine close items. This Algorithm can compute simultaneously frequent and frequent closed itemsets. It lays a foundation for efficiently mining irredundant rules.On the other nand, based on users' requirement and higher mining efficiency, this paper presents a definition of simple consequent association rules whose consequent includes just one item. The size of simple consequent association rules set is smaller than that of classical association rules mining algorithm. Moreover, all of the composed association rules can be produced. So not only no information is missed, but also only the rules that users are interested in are produced.Beginning from searching efficiency of association rule mining algorithms and usable of theirs, this paper did some tentative jobs. We need further search in the field in future.