304,723 research outputs found

    FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

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    In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

    Controlling False Positives in Association Rule Mining

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    Association rule mining is an important problem in the data mining area. It enumerates and tests a large number of rules on a dataset and outputs rules that satisfy user-specified constraints. Due to the large number of rules being tested, rules that do not represent real systematic effect in the data can satisfy the given constraints purely by random chance. Hence association rule mining often suffers from a high risk of false positive errors. There is a lack of comprehensive study on controlling false positives in association rule mining. In this paper, we adopt three multiple testing correction approaches---the direct adjustment approach, the permutation-based approach and the holdout approach---to control false positives in association rule mining, and conduct extensive experiments to study their performance. Our results show that (1) Numerous spurious rules are generated if no correction is made. (2) The three approaches can control false positives effectively. Among the three approaches, the permutation-based approach has the highest power of detecting real association rules, but it is very computationally expensive. We employ several techniques to reduce its cost effectively.Comment: VLDB201

    Mining for Useful Association Rules Using the ATMS

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    Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules

    Class Association Rules Mining based Rough Set Method

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    This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.Comment: 10 pages, 2 figure
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