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Evaluating Association Rules by Quantitative Pairwise Property Comparisons
2010 IEEE International Conference on Data Mining Workshops, 2010Evaluating association rules is an integral post process in association rule mining. Association rules are examined by measures for their interestingness. Different interestingness measures have been proposed. Given an association rule mining task, measures are assessed and selected against a set of user-specified properties.
Elnaz Delpisheh, John Z. Zhang
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An effective algorithm for mining interesting quantitative association rules
Proceedings of the 1997 ACM symposium on Applied computing - SAC '97, 1997In this paper, we describe a novel technique, called APACS2, for mining interesting quantitative association rules from very large databases. To effectively mine such rules, APACS2 employs adjusted difference analysis. The use of this technique has the advantage that it does not require any user-supplied thresholds which are often hard to determine ...
Keith C. C. Chan, Wai-Ho Au
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Mining Quantitative and Fuzzy Association Rules
2005The problem of mining association rules from databases was introduced by Agrawal, Imielinski, & Swami (1993). In this problem, we give a set of items and a large collection of transactions, which are subsets (baskets) of these items. The task is to find relationships between the occurrences of various items within those baskets.
Hong Shen, Susumu Horiguchi
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Effective Mining of Fuzzy Quantitative Weighted Association Rules
2010 International Conference on E-Business and E-Government, 2010This paper presents a new method of mining weighted association rules, which can hold the “weighted downward closed property” by using an improved model of weighted support measurements in the weighted setting. Compared to some generalized weighted association rules mining, it proves that the method can quickly and efficiently mine important ...
Chengjun Li, Tianqi Yang
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Analysis of Association Rule Mining on Quantitative Concept Lattice
2012In the process of association rule mining on rough set, it is always needed to deleting the reduplicative rows or columns, so supports and confidences of association rules cannot be obtained accurately. While the Hasse diagram of quantitative concept lattice contains all the objects and attributes information, supports of nodes can be obtained visually
Dexing Wang +3 more
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An Algorithm for Privacy-Preserving Quantitative Association Rules Mining
2006 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing, 2006When Data mining occurs on distributed data, privacy of parties becomes great concerns. This paper considers the problem of mining quantitative association rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of Privacy Preserving Data Mining (PPDM) research.
Weiwei Jing +4 more
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A fuzzy approach for mining quantitative association rules
Acta Cybern., 2001Summary: During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining.
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Experiences of Using a Quantitative Approach for Mining Association Rules
2003In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding.
Liang Dong, Christos Tjortjis
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Quantitative and Ordinal Association Rules Mining (QAR Mining)
2006Association rules have exhibited an excellent ability to identify interesting association relationships among a set of binary variables describing huge amount of transactions. Although the rules can be relatively easily generalized to other variable types, the generalization can result in a computationally expensive algorithm generating a prohibitive ...
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