Copertina di Clustering in non-metric spaces
Titolo del libro:

Clustering in non-metric spaces

From the Euclidean to the conceptual similarity

VDM Verlag Dr. Müller (05.08.2009 )

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ISBN-13:

978-3-639-18749-6

ISBN-10:
3639187490
EAN:
9783639187496
Lingua del libro:
Inglese
Risvolto di copertina:
Clustering algorithms partition a collection of objects into a certain number of clusters (groups, subsets, or categories). Object clustering algorithms generally partition a data set based on a dissimilarity measure expressed in terms of some distance. When the data distribution is irregular, for instance in image segmentation and pattern recognition where the nature of dissimilarity is conceptual rather than metric, distance functions may fail to drive correctly the clustering algorithm. Thus, the dissimilarity measure should be adapted to the specific data set. The purpose of this book is to present the main ideas concerning the application of the machine learning paradigm to the discovering of the dissimilarity between objects. Readers involved in similarity modeling will view how computational intelligence techniques, such as fuzzy systems, neural networks and evolutionary computation, can be a powerful vehicle for capturing conceptual relationships among objects. The application of such methods is also discussed in detail, with a series of experiments.
Casa editrice:
VDM Verlag Dr. Müller
Sito Web:
http://www.vdm-verlag.de
Da (autore):
Mario Giovanni C. A. Cimino
Numero di pagine:
104
Pubblicato il:
05.08.2009
Giacenza di magazzino:
Disponibile
categoria:
Tecnica
Prezzo:
49,00 €
Parole chiave:
Similarity Learning, Fuzzy Modeling, Neural Networks, Relational Clustering, Genetic Algorithms, Genetic Algorithms

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