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The Research On The Related Problems Of Association Rule Mining

Posted on:2010-12-03Degree:MasterType:Thesis
Country:ChinaCandidate:T J ZhangFull Text:PDF
GTID:2178360278981542Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
The association rule mining is a very important problem in data mining. The issue of mining frequent patterns plays a crucial role in association rule mining,sequential pattern mining, etc. Because of the time-consuming in mining frequent patterns, mining frequent closed patterns and mining maximal frequent patterns have been proposed to improve the mining efficiency. The set of frequent closed patterns or maximal frequent patterns is orders of magnitude smaller than the set of frequent patterns. The set of frequent closed patterns still contains enough information of the frequent patterns and its accurate support. The set of maximal frequent patterns contains all the set of the frequent patterns and there are applications where the set of maximal frequent patterns is adequate. In some applications, users may adjust the minimum support while database changed, and have to update the former mining results, so it is worth of studying in this case. Mining the interesting rules is another interesting issue. In all, it is very significative to do some researchs on those issues. In this paper, we have done some researches on the related problems of association rule mining. It is stated as follows:Firstly, two efficient algorithms FCI-Miner for mining frequent closed patterns and BFP-Miner for mining maximal frequent patterns are presented in this paper. The two algorithms all based on the improved FP-Tree (Frequent Pattern Tree) in order to compress and store the recorders of transaction database, and used depth-first search strategy without generating conditional FP-Trees and candidate patterns. The experimental evaluation on a number of real and synthetic databases shows that our algorithms outperform previous method in most cases.Secondly, a new integrated updating algorithm for mining maximal frequent patterns IUMFPA is proposed, which is aimed at handling the user adjusting the minimum support while database changes in order to find more useful maximal frequent patterns. It makes use of improved full FP-Tree structure and also utilizes the former FP-Tree and the mined results sufficiently. The experimental results indicate that IUMFPA performs efficiently.Finally, we propose a brief measure of rule interestingness to overcome the insufficient based on the support-confidence framework. It can determine the correlation and rarity of association rules, and especially be used to discover rules with strong correlation and high confidence, but low support. In the end, we take an example to demonstrate its effectiveness and practicality.
Keywords/Search Tags:Data mining, Association rules, Frequent pattern, Frequent closed pattern, Maximal frequent pattern, Intergraded updating mining, Frequent Pattern Tree (FP-Tree), Interestingness
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