304,723 research outputs found
FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases
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
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
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
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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