Algorithms for Fuzzy Clustering, Sadaaki Miyamoto; Hidetomo Ichihashi; Katsuhiro Ho

Название: Partitional clustering algorithms
ISBN: 3319092588 ISBN-13(EAN): 9783319092584
Издательство: Springer
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Цена: 130610.00 T
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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: Ujjwal Maulik; Sanghamitra Bandyopadhyay; Anirban
Название: Multiobjective Genetic Algorithms for Clustering
ISBN: 3642439632 ISBN-13(EAN): 9783642439636
Издательство: Springer
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Цена: 51200.00 T
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Описание: This book covers clustering using multiobjective genetic algorithms, with extensive real-life application in data mining and bioinformatics. The authors offer instructions for relevant techniques, and demonstrate real-world applications in several disciplines.

Автор: Guojun Gan
Название: Data Clustering: Theory, Algorithms, and Applications
ISBN: 0898716233 ISBN-13(EAN): 9780898716238
Издательство: Mare Nostrum (Eurospan)
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Цена: 112860.00 T
Наличие на складе: Невозможна поставка.
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.

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: 89760.00 T
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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: Dmitri A. Viattchenin
Название: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
ISBN: 364244301X ISBN-13(EAN): 9783642443015
Издательство: Springer
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Цена: 113180.00 T
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Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.

Автор: M. Emre Celebi
Название: Partitional Clustering Algorithms
ISBN: 3319347985 ISBN-13(EAN): 9783319347981
Издательство: Springer
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Цена: 113180.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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: ?gnes Vathy-Fogarassy; J?nos Abonyi
Название: Graph-Based Clustering and Data Visualization Algorithms
ISBN: 1447151577 ISBN-13(EAN): 9781447151579
Издательство: Springer
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Цена: 55890.00 T
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Описание:

Vector Quantisation and Topology-Based Graph Representation

Graph-Based Clustering Algorithms

Graph-Based Visualisation of High-Dimensional Data

Автор: Viattchenin Dmitri A
Название: Heuristic Approach to Possibilistic Clustering: Algorithms a
ISBN: 3642355358 ISBN-13(EAN): 9783642355356
Издательство: Springer
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Цена: 130610.00 T
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Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.

Автор: Zeshui Xu
Название: Intuitionistic Fuzzy Aggregation and Clustering
ISBN: 3642436129 ISBN-13(EAN): 9783642436123
Издательство: Springer
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Цена: 144410.00 T
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Описание: An inclusive primer on intuitionistic fuzzy clustering algorithms, this volume covers priority theory and methods of intuitionistic preference relations. It also shows how fuzzy algorithms can be applied to practicalities such as supply-chain management.

Автор: Lazzerini
Название: Fuzzy Sets & their Application to Clustering & Training
ISBN: 0849305896 ISBN-13(EAN): 9780849305894
Издательство: Taylor&Francis
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Цена: 178640.00 T
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Описание: Fuzzy logic applications allow uncertain or imprecise data to be clustered and analyzed when traditional methods cannot be used. This volume offers an introduction to fuzzy set theory and then progresses through the algorithms and techniques used to manipulate data using fuzzy sets, including classification, hierarchy, and cluster structure.

Автор: Vicen? Torra; Anders Dahlbom; Yasuo Narukawa
Название: Fuzzy Sets, Rough Sets, Multisets and Clustering
ISBN: 3319475568 ISBN-13(EAN): 9783319475561
Издательство: Springer
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Цена: 139750.00 T
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Описание: Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making.The book is divided in four parts, the first of which focuses on clustering and classification.

Автор: Mika Sato-Ilic
Название: Innovations in Fuzzy Clustering
ISBN: 3642070728 ISBN-13(EAN): 9783642070723
Издательство: Springer
Цена: 135090.00 T
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Описание: Clustering has been around for many decades and located itself in a uniquepositionasafundamentalconceptualandalgorithmiclandmark of data analysis. Almost since the very inception of fuzzy sets, the role and potential of these information granules in revealing and describing structureindatawasfullyacknowledgedandappreciated.Asamatter of fact, with the rapid growth of volumes of digital information, the role of clustering becomes even more visible and critical. Furthermore given the anticipated human centricity of the majority of artifacts of digitaleraandacontinuousbuildupofmountainsofdata, onebecomes fully cognizant of the growing role and an enormous potential of fuzzy sets and granular computing in the design of intelligent systems. In therecentyearsclusteringhasundergoneasubstantialmetamorphosis. Frombeinganexclusivelydata drivenpursuit, ithastransformeditself into a vehicle whose data centricity has been substantially augmented by the incorporation of domain knowledge thus giving rise to the next generation of knowledge-oriented and collaborative clustering. Interestingly enough, fuzzy clustering exhibits a dominant role in many developments of the technology of fuzzy sets including fuzzy modeling, fuzzy control, data mining, pattern recognition, and image processing. When browsing through numerous papers on fuzzy m- eling we can witness an important trend of a substantial reliance on fuzzy clustering being regarded as the general development tool. The same central position of fuzzy clustering becomes visible in pattern classi?ers and neurofuzzy systems. All in all, it becomes evident that further progress in fuzzy clustering is of vital relevance and bene't to the overall progress of the area of fuzzy sets and their application