Graph-Based Clustering and Data Visualization Algorithms, ?gnes Vathy-Fogarassy; J?nos Abonyi

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
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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.

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: 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: 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: M. Emre Celebi
Название: Partitional Clustering Algorithms
ISBN: 3319347985 ISBN-13(EAN): 9783319347981
Издательство: Springer
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Цена: 113180.00 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: Sadaaki Miyamoto; Hidetomo Ichihashi; Katsuhiro Ho
Название: Algorithms for Fuzzy Clustering
ISBN: 3642097537 ISBN-13(EAN): 9783642097539
Издательство: Springer
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Цена: 139750.00 T
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Описание: Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose, which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means, andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral, regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.

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: Riesen Kaspar & Bunke Horst
Название: Graph Classification And Clustering Based On Vector Space Embedding
ISBN: 9814304719 ISBN-13(EAN): 9789814304719
Издательство: World Scientific Publishing
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Цена: 116160.00 T
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Описание: Focuses on a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. This title aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.

Автор: Thomas Villmann; M. Biehl; Barbara Hammer; Michel
Название: Similarity-Based Clustering
ISBN: 3642018041 ISBN-13(EAN): 9783642018046
Издательство: Springer
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Цена: 95770.00 T
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Описание: Recent Developments and Biomedical Applications. .

Автор: Isra?l C?sar Lerman
Название: Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
ISBN: 1447167910 ISBN-13(EAN): 9781447167914
Издательство: Springer
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Цена: 153720.00 T
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Описание: Preface.- On Some Facets of the Partition Set of a Finite Set.- Two Methods of Non-hierarchical Clustering.- Structure and Mathematical Representation of Data.- Ordinal and Metrical Analysis of the Resemblance Notion.- Comparing Attributes by a Probabilistic and Statistical Association I.- Comparing Attributes by a Probabilistic and Statistical Association II.- Comparing Objects or Categories Described by Attributes.- The Notion of "Natural" Class, Tools for its Interpretation. The Classifiability Concept.- Quality Measures in Clustering.- Building a Classification Tree.- Applying the LLA Method to Real Data.- Conclusion and Thoughts for Future Works