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A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications [electronic resource] / by Dmitri A. Viattchenin.

By: Viattchenin, Dmitri A [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Fuzziness and Soft Computing: 297Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XII, 227 p. 98 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642355363.Subject(s): Engineering | Data mining | Artificial intelligence | Engineering | Computational Intelligence | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics)DDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Heuristic Algorithms of Possibilistic Clustering -- Clustering Approaches for the Uncertain Data -- Applications of the Heuristic Algorithms of Possibilistic Clustering.
In: Springer eBooksSummary: 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
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Introduction -- Heuristic Algorithms of Possibilistic Clustering -- Clustering Approaches for the Uncertain Data -- Applications of the Heuristic Algorithms of Possibilistic Clustering.

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

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