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On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery, 1998The discovery of quantitative association rules in large databases is considered an interesting and important research problem. Recently, different aspects of the problem have been studied, and several algorithms have been presented in the literature, among others in (Srikant and Agrawal, 1996; Fukuda et al., 1996a; Fukuda et al., 1996b; Yoda et al ...
Jef Wijsen, Robert Meersman
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Discovering and managing quantitative association rules
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013Although association rule mining has been studied in the literature for quite a while and numerical attributes are prevalent, perhaps surprisingly, the state-of-the-art quantitative association rule mining is rather inefficient and ineffective in discovering all useful rules.
Chunyao Song, Tingjian Ge
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Mining fuzzy quantitative association rules
Expert Systems, 2006Abstract: The concept of fuzzy sets is one of the most fundamental and influential tools in the development of computational intelligence. In this paper the fuzzy pincer search algorithm is proposed. It generates fuzzy association rules by adopting combined top‐down and bottom‐up approaches.
R. B. V. Subramanyam, A. Goswami
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Association Rule and Quantitative Association Rule Mining among Infrequent Items
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007), 2007Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the ...
Ling Zhou, Stephen Yau
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Mining association rules from quantitative data☆
Intelligent Data Analysis, 1999Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications.
Tzung-Pei Hong +2 more
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Trends in quantitative association rule mining techniques
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015Association rule mining (ARM) techniques are effective in extracting frequent patterns and hidden associations among data items in various databases. These techniques are widely used for learning behavior, predicting events and making decisions at various levels.
Dhrubajit Adhikary, Swarup Roy
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Mining fuzzy quantitative association rules
Proceedings 11th International Conference on Tools with Artificial Intelligence, 2003Given a relational database and a set of fuzzy terms defined for some attributes we consider the problem of mining fuzzy quantitative association rules that may contain crisp values, intervals, and fuzzy terms in both antecedent and consequent. We present an algorithm extended from the equi-depth partition (EDP) algorithm for solving this problem.
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Preserving Privacy in Mining Quantitative Associations Rules
International Journal of Information Security and Privacy, 2009Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association ...
Madhu Ahluwalia +2 more
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An Analysis of Quantitative Measures Associated with Rules
1999In this paper, we analyze quantitative measures associated with if-then type rules. Basic quantities are identified and many existing measures are examined using the basic quantities. The main objective is to provide a synthesis of existing results in a simple and unified framework.
Yiyu Yao, Ning Zhong 0001
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Application of cluster in quantitative association rules mining
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010On mining quantitative association rules and the segmentation of numerical attributes variables of ordered data, Fisher cluster method can be used to determine the segmentation range and the segmentation number of this variable value. The method takes the data interval and data density into account.
Lanfang Lou +3 more
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