The Resource Partitional clustering algorithms, M. Emre Celebi, editor

Partitional clustering algorithms, M. Emre Celebi, editor

Label
Partitional clustering algorithms
Title
Partitional clustering algorithms
Statement of responsibility
M. Emre Celebi, editor
Contributor
Editor
Subject
Genre
Language
eng
Summary
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in realistic applications; Discusses algorithms specifically designed for partitional clustering; Covers center-based, competitive learning, density-based, fuzzy, graph-based, grid-based, metaheuristic, and model-based approaches
Cataloging source
N$T
Dewey number
005.7
Illustrations
illustrations
Index
no index present
LC call number
TK5105.7
LC item number
.P37 2015eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Celebi, M. Emre
http://library.link/vocab/subjectName
  • Cluster analysis
  • Computer algorithms
  • COMPUTERS
  • Cluster analysis
  • Computer algorithms
  • Engineering
  • Communications Engineering, Networks
  • Information Systems and Communication Service
  • Signal, Image and Speech Processing
  • Computer networking & communications
  • Imaging systems & technology
  • Communications engineering / telecommunications
Label
Partitional clustering algorithms, M. Emre Celebi, editor
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-09259-1
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Recent developments in model-based clustering with applications -- Accelerating Lloyd?s algorithm for k-means clustering -- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm -- Nonsmooth optimization based algorithms in cluster analysis -- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data -- Density Based Clustering: Alternatives to DBSCAN -- Nonnegative matrix factorization for interactive topic modeling and document clustering -- Overview of overlapping partitional clustering methods -- On Semi-Supervised Clustering -- Consensus of Clusterings based on High-order Dissimilarities -- Hubness-Based Clustering of High-Dimensional Data -- Clustering for Monitoring Distributed Data Streams
Dimensions
unknown
Extent
1 online resource (425 pages)
File format
unknown
Form of item
online
Isbn
9783319092591
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-09259-1
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)894894388
  • (OCoLC)ocn894894388
Label
Partitional clustering algorithms, M. Emre Celebi, editor
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-09259-1
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Recent developments in model-based clustering with applications -- Accelerating Lloyd?s algorithm for k-means clustering -- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm -- Nonsmooth optimization based algorithms in cluster analysis -- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data -- Density Based Clustering: Alternatives to DBSCAN -- Nonnegative matrix factorization for interactive topic modeling and document clustering -- Overview of overlapping partitional clustering methods -- On Semi-Supervised Clustering -- Consensus of Clusterings based on High-order Dissimilarities -- Hubness-Based Clustering of High-Dimensional Data -- Clustering for Monitoring Distributed Data Streams
Dimensions
unknown
Extent
1 online resource (425 pages)
File format
unknown
Form of item
online
Isbn
9783319092591
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-09259-1
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)894894388
  • (OCoLC)ocn894894388

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