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Fuzzy Sets, Rough Sets, Multisets and Clustering [2017]
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On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets
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Contributions of Fuzzy Concepts to Data Clustering
- 29
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Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-Induced Algorithms
- 45
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Semi-supervised Fuzzy <Emphasis Type="Italic">c</Emphasis>-Means Algorithms by Revising Dissimilarity/Kernel Matrices
- 63
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Various Types of Objective-Based Rough Clustering
- 87
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On Some Clustering Algorithms Based on Tolerance
- 101
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Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
- 123
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Consensus-Based Agglomerative Hierarchical Clustering
- 137
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Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach
- 157
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On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
- 169
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Experiences Using Decision Trees for Knowledge Discovery
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L-Fuzzy Bags
- 221
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A Perspective on Differences Between Atanassov’s Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets
- 241
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Attribute Importance Degrees Corresponding to Several Kinds of Attribute Reduction in the Setting of the Classical Rough Sets
- 257
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A Review on Rough Set-Based Interrelationship Mining
- 277
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OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method
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A Dynamic Average Value-at-Risk Portfolio Model with Fuzzy Random Variables
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Group Decision Making: Consensus Approaches Based on Soft Consensus Measures
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Construction of Capacities from Overlap Indexes
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Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance