Journal Article10.1002/WIDM.34
Fuzzy machine learning and data mining a
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TL;DR: The aim of this paper is to assess the relevance of fuzzy set theory and fuzzy logic for these fields, highlighting potential contributions without concealing alleged limitations and shortcomings of current research.
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Abstract: The development of methods for machine learning and data mining has attracted increasing attention in the fuzzy set community in recent years. The aim of this paper is to assess the relevance of fuzzy set theory and fuzzy logic for these fields, highlighting potential contributions without concealing alleged limitations and shortcomings of current research. To this end, some typical applications of fuzzy logic will be reviewed, followed by a more systematic discussion of possible benefits of fuzzy methods. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 269–283 DOI: 10.1002/widm.34
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References
•Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 12 Sep 1994
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
•Book
Metamathematics of Fuzzy Logic
Petr Hájek
- 31 Aug 1998
TL;DR: This paper presents a meta-analysis of many-Valued Propositional Logic, focusing on the part of Lukasiewicz's Logic that deals with Complexity, Undecidability and Generalized Quantifiers and Modalities.
3.9K
Multi-label classification: An overview
TL;DR: The task of multi-label classification is introduced, the sparse related literature is organizes into a structured presentation and comparative experimental results of certain multilabel classification methods are performed.
A possibilistic approach to clustering
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
2.5K