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Fuzzy Sets, Rough Sets, Multisets and Clustering [2017]

1
On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets
9
Contributions of Fuzzy Concepts to Data Clustering
29
Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-Induced Algorithms
45
Semi-supervised Fuzzy <Emphasis Type="Italic">c</Emphasis>-Means Algorithms by Revising Dissimilarity/Kernel Matrices
63
Various Types of Objective-Based Rough Clustering
87
On Some Clustering Algorithms Based on Tolerance
101
Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition
123
Consensus-Based Agglomerative Hierarchical Clustering
137
Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach
157
On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
169
Experiences Using Decision Trees for Knowledge Discovery
195
L-Fuzzy Bags
221
A Perspective on Differences Between Atanassov’s Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets
241
Attribute Importance Degrees Corresponding to Several Kinds of Attribute Reduction in the Setting of the Classical Rough Sets
257
A Review on Rough Set-Based Interrelationship Mining
277
OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method
291
A Dynamic Average Value-at-Risk Portfolio Model with Fuzzy Random Variables
307
Group Decision Making: Consensus Approaches Based on Soft Consensus Measures
323
Construction of Capacities from Overlap Indexes
337
Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance
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