Genome Clustering, Alexander Bolshoy; Zeev Volkovich; Valery Kirzhner

Автор: Felipe M. G. Fran?a; Alberto Ferreira de Souza
Название: Intelligent Text Categorization and Clustering
ISBN: 3642099297 ISBN-13(EAN): 9783642099298
Издательство: Springer
Рейтинг:
Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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: Ronis D
Название: Clustering Standards in Integrated Units: Second Edition
ISBN: 1412955564 ISBN-13(EAN): 9781412955560
Издательство: Sage Publications
Рейтинг:
Цена: 68640.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides teachers with a framework for designing, implementing, and evaluating interdisciplinary units that integrate content and standards across multiple curriculum areas.

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
Рейтинг:
Цена: 89760.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: * In this book, authors Rachev, Kim, Bianchi, and Fabozzi present readers with the notions of risk and their corresponding performance measures.

Автор: Toshiro Tango
Название: Statistical Methods for Disease Clustering
ISBN: 1461425565 ISBN-13(EAN): 9781461425564
Издательство: Springer
Рейтинг:
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a modern perspective on statistical methods for detecting disease clustering. It offers analysis and illustration of methods for a variety of real data sets, and will provide an invaluable resource for a wide ranging of audience.

Автор: Felipe M. G. Fran?a; Alberto Ferreira de Souza
Название: Intelligent Text Categorization and Clustering
ISBN: 3540856439 ISBN-13(EAN): 9783540856436
Издательство: Springer
Рейтинг:
Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers have employed many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing. This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering.

Автор: Guojun Gan
Название: Data Clustering: Theory, Algorithms, and Applications
ISBN: 0898716233 ISBN-13(EAN): 9780898716238
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
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: Saman K. Halgamuge; Lipo Wang
Название: Classification and Clustering for Knowledge Discovery
ISBN: 3642065422 ISBN-13(EAN): 9783642065422
Издательство: Springer
Рейтинг:
Цена: 194730.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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.