Fuzzy Sets, Rough Sets, Multisets and Clustering

On this book: clustering, multisets, rough sets and fuzzy sets.- Part 1: Clustering and Classification.- Contributions of Fuzzy Concepts to Data Clustering.- Fuzzy Clustering/Co-clustering and Probabilistic Mixture Models-induced Algorithms.- Semi-Supervised Fuzzy c-Means Algorithms by Revising Dissimilarity/Kernel Matrices.- Various Types of Objective-Based Rough Clustering.- On Some Clustering Algorithms Based on Tolerance.- Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition.- Consensus-based agglomerative hierarchical clustering.- Using a reverse engineering type paradigm in clustering. An evolutionary pro-gramming based approach.- On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data.- Experiences using Decision Trees for Knowledge Discovery.- Part 2: Bags, Fuzzy Bags, and Some Other Fuzzy Extensions.- L-fuzzy Bags.- A Perspective on Differences between Atanassov's Intuitionistic Fuzzy Sets and Interval-valued Fuzzy Sets.- Part 3: Rough Sets.-Attribute Importance Degrees Corresponding to Several Kinds of Attribute Reduction in the Setting of the Classical Rough Sets.- A Review on Rough Set-based Interrelationship Mining.- Part 4: Fuzzy sets and decision making.- OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method.- A dynamic average value-at-risk portfolio model with fuzzy random variables.- Group Decision Making: Consensus Approaches based on Soft Consensus Measures.- Construction of capacities from overlap indexes.- Clustering alternatives and learning preferences based on decision attitudes and weighted overlap dominance.