Advances in network clustering and blockmodeling /, Patrick Doreian

Автор: Rachev, Svetlozar T. Kim, Young Shim Bianchi, Mich
Название: Financial models with levy processes and volatility clustering
ISBN: 0470482354 ISBN-13(EAN): 9780470482353
Издательство: Wiley
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Цена: 13464 р.
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Описание: * In this book, authors Rachev, Kim, Bianchi, and Fabozzi present readers with the notions of risk and their corresponding performance measures.

Автор: Guojun Gan
Название: Data Clustering: Theory, Algorithms, and Applications
ISBN: 0898716233 ISBN-13(EAN): 9780898716238
Издательство: Eurospan
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Цена: 18340 р.
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Описание: Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.

Название: Partitional clustering algorithms
ISBN: 3319092588 ISBN-13(EAN): 9783319092584
Издательство: Springer
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Цена: 19591 р.
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Описание: This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering.

Автор: Felipe M. G. Fran?a; Alberto Ferreira de Souza
Название: Intelligent Text Categorization and Clustering
ISBN: 3642099297 ISBN-13(EAN): 9783642099298
Издательство: Springer
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Цена: 20962 р.
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Описание: Spam filtering and web search are well-known applications of text categorization and clustering, but there are also many lesser known everyday uses. This text covers a wide spectrum of recent research developed for the field.

Автор: Saman K. Halgamuge; Lipo Wang
Название: Classification and Clustering for Knowledge Discovery
ISBN: 3642065422 ISBN-13(EAN): 9783642065422
Издательство: Springer
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Цена: 29209 р.
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Описание: This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees.

Автор: Ronis D
Название: Clustering Standards in Integrated Units: Second Edition
ISBN: 1412955564 ISBN-13(EAN): 9781412955560
Издательство: Sage Publications
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Цена: 10296 р.
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Описание: Provides teachers with a framework for designing, implementing, and evaluating interdisciplinary units that integrate content and standards across multiple curriculum areas.

Автор: Marten Brenner; Tom L?nnroth; F.Bary Malik
Название: Clustering Phenomena in Atoms and Nuclei
ISBN: 3662028298 ISBN-13(EAN): 9783662028292
Издательство: Springer
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Цена: 16979 р.
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Описание: In these days of specialization it is important to bring together physicists working in diverse areas to exchange and share their ideas and excitement. The actual idea to explore the possibility of holding a conference, however, crystallized in the summer of 1989 during a visit of FBM to Abo Akademi.

Автор: Mirkin, Boris
Название: Clustering for Data Mining
ISBN: 1584885343 ISBN-13(EAN): 9781584885344
Издательство: Taylor&Francis
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Цена: 9186 р.
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Описание: Presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of interest, such as clustering mixed scale data and incomplete clustering. This work suggests methods for both cluster finding and cluster description, and includes nearly 60 computational examples covering the various stages of clustering.