| Description |
1 online resource |
| Series |
Studies in fuzziness and soft computing, 1434-9922 ; 297 |
|
Studies in fuzziness and soft computing ; 297.
|
| Contents |
Introduction -- Heuristic algorithms of possibilistic clustering -- Clustering approaches for uncertain data -- Applications of heuristic algorithms of possibilistic clustering |
| Summary |
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 |
| Analysis |
Engineering |
|
Data mining |
|
Artificial intelligence |
|
Computational Intelligence |
| Bibliography |
Includes bibliographical references |
| Notes |
English |
| Subject |
Fuzzy sets.
|
|
Cluster analysis -- Data processing
|
|
Electronic data processing.
|
|
Ingénierie.
|
|
Cluster analysis -- Data processing
|
|
Electronic data processing
|
|
Fuzzy sets
|
| Form |
Electronic book
|
| ISBN |
9783642355363 |
|
3642355366 |
|
3642355358 |
|
9783642355356 |
|
