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The Resource Grouping multidimensional data : recent advances in clustering, Jacob Kogan, Charles Nicholas, Marc Teboulle (eds.), (electronic resource)

Grouping multidimensional data : recent advances in clustering, Jacob Kogan, Charles Nicholas, Marc Teboulle (eds.), (electronic resource)

Contributor
Summary
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
Language
eng
Work
Publication
Extent
1 online resource (xii, 268 pages)
Contents
  • 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
Isbn
9783540283485
Instance
Label
Grouping multidimensional data : recent advances in clustering
Title
Grouping multidimensional data
Title remainder
recent advances in clustering
Statement of responsibility
Jacob Kogan, Charles Nicholas, Marc Teboulle (eds.)
Contributor
Provider
Subject
Genre
Language
eng
Summary
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
Member of
Is part of
Cataloging source
GW5XE
Image bit depth
0
LC call number
QA278
LC item number
.G82 2006eb
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
  • 1954-
  • 1957-
http://library.link/vocab/relatedWorkOrContributorName
  • SpringerLink
  • Kogan, Jacob
  • Nicholas, Charles K.
  • Teboulle, M
http://library.link/vocab/subjectName
  • Cluster analysis
  • Data mining
  • Classification automatique (Statistique)
  • Exploration de données (Informatique)
  • MATHEMATICS
  • Informatique
  • Gegevensverzameling
  • Gegevensmodellering
  • Computer Science
  • Information Storage and Retrieval
  • Math Applications in Computer Science
  • Pattern Recognition
  • Computer science
  • Information storage and retrieval systems
  • Optical pattern recognition
  • Data mining
  • Electronic books
  • Cluster analysis
Label
Grouping multidimensional data : recent advances in clustering, Jacob Kogan, Charles Nicholas, Marc Teboulle (eds.), (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references (pages 239-264) and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
1 online resource (xii, 268 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783540283485
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-540-28348-5
System control number
  • (OCoLC)262692110
  • (OCoLC)ocn262692110
  • (DE-He213)3-540-28349-8
Label
Grouping multidimensional data : recent advances in clustering, Jacob Kogan, Charles Nicholas, Marc Teboulle (eds.), (electronic resource)
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references (pages 239-264) and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
1 online resource (xii, 268 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9783540283485
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-3-540-28348-5
System control number
  • (OCoLC)262692110
  • (OCoLC)ocn262692110
  • (DE-He213)3-540-28349-8

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