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Kogan, Jacob / Nicholas, Charles / Teboulle, Marc (eds.):

Grouping Multidimensional Data - encadernada, livro de bolso

2005, ISBN: 9783540283485

[ED: Hardcover], [PU: Springer, Berlin], Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to dis… mais…

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Grouping Multidimensional Data Recent Advances in Clustering - Kogan, Jacob (Herausgeber); Teboulle, Marc (Herausgeber); Nicholas, Charles (Herausgeber)
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Kogan, Jacob (Herausgeber); Teboulle, Marc (Herausgeber); Nicholas, Charles (Herausgeber):

Grouping Multidimensional Data Recent Advances in Clustering - encadernada, livro de bolso

2005, ISBN: 354028348X

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Kogan, Jacob [Editor]; Nicholas, Charles [Editor]; Teboulle, Marc [Editor];:
Grouping Multidimensional Data: Recent Advances in Clustering - encadernada, livro de bolso

2005

ISBN: 9783540283485

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Kogan, Jacob [Editor]; Nicholas, Charles [Editor]; Teboulle, Marc [Editor];:
Grouping Multidimensional Data: Recent Advances in Clustering - encadernada, livro de bolso

ISBN: 9783540283485

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Grouping Multidimensional Data: Recent Advances in Clustering - encadernada, livro de bolso

2005, ISBN: 354028348X

[EAN: 9783540283485], Neubuch, [PU: Springer], Well packaged and promptly shipped from California. US veteran operated., Books

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Grouping Multidimensional Data
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9783540283485


Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas. TOC:Foreword (J. Han).- The Star Clustering Algorithm for Information Organization (J. A. Aslam, E. Pelekhov, D. Rus).- A Survey of Clustering Data Mining Techniques (P. Berkhin).- Similarity-based Text Clustering: A Comparitive Study (J. Ghosh, A. Strehl).- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning (D. Littau, D. Boley).- Clustering with Entropy-like k-means ALgorithms (M. Teboulle, P. Berkhin, I. Dhillon, Y. Guan, J. Kogan).- Sampling Methods for Building Initial Partitions (Z. Volkovich, J. Kogan, Ch. Nicholas).- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections (D. Zeimpekis, E. Gallopoulos).- Criterion Functions for Clustering on High Dimensional Data (Y. Zhao, G. Karypis).- Index.

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EAN (ISBN-13): 9783540283485
ISBN (ISBN-10): 354028348X
Livro de capa dura
Ano de publicação: 2005
Editor/Editora: Berlin, Springer, 2005.

Livro na base de dados desde 2007-05-13T14:20:48-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-07-04T16:22:01-03:00 (Sao Paulo)
Número ISBN/EAN: 9783540283485

Número ISBN - Ortografia alternativa:
3-540-28348-X, 978-3-540-28348-5
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: kogan, datta, nicholas
Título do livro: grouping multidimensional data, recent advances

Dados da editora

Autor: Jacob Kogan; Charles Nicholas; Marc Teboulle
Título: Grouping Multidimensional Data - Recent Advances in Clustering
Editora: Springer; Springer Berlin
268 Páginas
Ano de publicação: 2005-12-16
Berlin; Heidelberg; DE
Língua: Inglês
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XII, 268 p.

BB; Hardcover, Softcover / Informatik, EDV/Informatik; Algorithmen und Datenstrukturen; Verstehen; Excel; LA; MATLAB; algorithms; classification; clustering algorithm; correlation; data analysis; data clustering; data mining; text clustering; text mining; Data Structures and Information Theory; Information Storage and Retrieval; Statistical Theory and Methods; Mathematical Applications in Computer Science; Statistics and Computing; Automated Pattern Recognition; Informationstheorie; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Wahrscheinlichkeitsrechnung und Statistik; Theoretische Informatik; Mathematische und statistische Software; Mustererkennung; EA; BC

The Star Clustering Algorithm for Information Organization.- A Survey of Clustering Data Mining Techniques.- Similarity-Based Text Clustering: A Comparative Study.- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning.- Clustering with Entropy-Like k-Means Algorithms.- Sampling Methods for Building Initial Partitions.- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections.- Criterion Functions for Clustering on High-Dimensional Data.
Includes supplementary material: sn.pub/extras

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