Study Of The Infrequent Association Rule Mining

In this paper, I study the typical theory and some other problem that are waited to be solved about data mining and association rule mining, and put the important on the study of infrequent association rule mining.For simply,data mining is the nontrivial extraction or mining knowledge from large amount of data.Data mining is a major research domain of computer science since the latter 80s'.It develops from many disciplines such as database technology, artificial intelligence and statistics,etc..Association rule mining is a major function of data mining.It is first introduced by Agrawal et.al in the year 1993 and used to discover the interest relation or rules between items of large data set. Association rule is used to analyze the consumer purchasing pattern in retail stores, tactics analyzing and business management and now, association rule mining have become a prevail tools of understanding data.We discuss some traditional algorithm of association rule mining: Apriori algorithm, FP-G algorithm and multi-layer association rule mining algorithm. We also summarize and introduce some achievement in the study of mining association rule in recent years, such as Using the technology of multiple similarity queries to enhance the efficiency of data mining. Mining the quantitative association rules with the improved Apriori algorithm, Mining association rules based on the framework of collective and confidence. Mining mutually dependent patterns, etc..Some problem that association rule mining confront are required to be solved. In this paper,we mainly study the problem: Infrequent association rule mining, Raising the capability of algorithm-user exchange and automatic level of mining progress, and give the corresponding algorithms or methods. In this paper,we put premium on the study of mining association rule based on the framework of similarity-confidence in the study of infrequent association rule mining, we give out the extension algorithm of mining similarity association rule and some relevant theory. In the subject of raising the automation level of mining progress, we mainly study how to use the technology of interpolation to determine the threshold of support/confidence automaticly, And give some detailed experimental examples.