A Heuristic Approach To Possibilistic Clustering Algorithms And Applications
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A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications
A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications
Novel Developments in Uncertainty Representation and Processing
This volume contains, first of all, the papers presented at the Fourteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN-2015) held on October 26-28, 2015 in Cracow, Poland. Moreover, the volume contains some papers of a particular relevance not presented at the Workshop. The Workshop is mainly devoted to the presentation of recent research results in the broadly perceived fields of intuitionistic fuzzy sets and generalized nets initiated by Professor Krassimir T. Atanassov whose constant inspiration and support is crucial for such a widespread growing popularity and recognition of these areas. The Workshop is a next edition of a series of the IWIFSGN Workshops organized for years by the Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria, and WIT -- Warsaw School of Information Technology, Warsaw, Poland, and co-organized by: Matej Bel University, Banska Bystrica, Slovakia, Universidad Publica de Navarra, Pamplona, Spain, Universidade de Tras-Os-Montes e Alto Douro, Vila Real, Portugal, Prof. Asen Zlatarov University, Burgas, Bulgaria, Complutense University, Madrid, Spain, and the University of Westminster, Harrow, UK.