Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering, Isra?l C?sar Lerman

Автор: Francesco Masulli; Alfredo Petrosino; Stefano Rove
Название: Clustering High--Dimensional Data
ISBN: 3662485761 ISBN-13(EAN): 9783662485767
Издательство: Springer
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Цена: 5590.00 р.
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Описание: This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.

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: 14365.00 р.
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Описание: Recent Developments and Biomedical Applications. .

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: 7680.00 р.
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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: Unwin
Название: Graphical Data Analysis with R
ISBN: 1498715230 ISBN-13(EAN): 9781498715232
Издательство: Taylor&Francis
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Цена: 11482.00 р.
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Описание:

See How Graphics Reveal Information

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.

Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

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: 16977.00 р.
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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: Saito
Название: Data Analysis of Asymmetric Structures
ISBN: 0824753984 ISBN-13(EAN): 9780824753986
Издательство: Taylor&Francis
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Цена: 22202.00 р.
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Описание: Offers a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications. This work details theories, methods, and models for the analysis of asymmetric structures in a variety of disciplines and presents opportunities and challenges affecting research developments and business applications.

Автор: Junjie Wu
Название: Advances in K-means Clustering
ISBN: 3642447570 ISBN-13(EAN): 9783642447570
Издательство: Springer
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Цена: 15372.00 р.
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Описание: The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of `big data`, detailing a theoretical framework for aggregating and validating clusters with K-means.

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: 19591.00 р.
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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.