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Determination of rule weights of fuzzy association rules
10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002In this paper, first we extend two basic measures of association rules in data mining (i.e, confidence and support) to the case of fuzzy association rules. The main difference between standard and fuzzy association rules is the discretization of continuous variables.
Hisao Ishibuchi +2 more
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On fuzzy association rules based on fuzzy cardinalities
10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002The paper discusses the benefit of using fuzzy sets in data summaries based on generalized association rules. Fuzzy sets provide a convenient interface between labels and data and allow for partial belonging to connex but distinct classes. They thus offer a robust reading of the data.
Bosc, Patrick +3 more
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A Survey on Fuzzy Association Rule Mining
International Journal of Data Warehousing and Mining, 2013Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge.
Harihar Kalia +2 more
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Discovery of Fuzzy Temporal Association Rules
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004We propose a data mining system for discovering interesting temporal patterns from large databases. The mined patterns are expressed in fuzzy temporal association rules which satisfy the temporal requirements specified by the user. Temporal requirements specified by human beings tend to be ill-defined or uncertain. To deal with this kind of uncertainty,
Wan-Jui Lee, Shie-Jue Lee
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A prediction method of fuzzy association rules
Proceedings Fifth IEEE Workshop on Mobile Computing Systems and Applications, 2004Quantitative attributes are partitioned into several fuzzy sets by c-means algorithm, and search technology of Apriori algorithm is improved to discover interesting fuzzy association rules. The first prediction method of fuzzy association rules is presented, and shortcoming of this prediction method is analyzed.
Jianjiang Lu, Baowen Xu, Jixiang Jiang
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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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Mining Fuzzy Weighted Association Rules
2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07), 2007The paper combines and extends the technologies of fuzzy sets and association rules, considering users' differential emphasis on each attribute through fuzzy regions. A fuzzy data mining algorithm is proposed to discovery fuzzy association rules for weighted quantitative data.
David L. Olson, Yanhong Li
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Learning Fuzzy Association Rules and Associative Classification Rules
2006 IEEE International Conference on Fuzzy Systems, 2006Learning association rules and/or associative classification rules has been extensively studied in data mining and knowledge discovery community. Associative classification rules are considered as constrained association rules. Mining traditional association rules from transaction databases, however, has suffered some limitations.
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Mining fuzzy periodic association rules
Data & Knowledge Engineering, 2008We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e.,
Wan-Jui Lee, Jung-Yi Jiang, Shie-Jue Lee
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Fuzzy Functional Dependencies and Fuzzy Association Rules
1999This paper proposes fuzzy association rule which is a more generalized concept than boolean, quantitative, and interval association rules. Fuzzy association rule is a spectrum of definitions. Each particular fuzzy association rule can be defined by adding restrictions on the fuzziness depending on the needs of practical situations.
Yuping Yang, Mukesh Singhal
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