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Partitional Clustering Algorithms

Overview of attention for book
Cover of 'Partitional Clustering Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Recent Developments in Model-Based Clustering with Applications
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    Chapter 2 Accelerating Lloyd’s Algorithm for k -Means Clustering
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    Chapter 3 Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
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    Chapter 4 Nonsmooth Optimization Based Algorithms in Cluster Analysis
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    Chapter 5 Fuzzy Clustering Algorithms and Validity Indices for Distributed Data
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    Chapter 6 Density Based Clustering: Alternatives to DBSCAN
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    Chapter 7 Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering
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    Chapter 8 Overview of Overlapping Partitional Clustering Methods
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    Chapter 9 On Semi-Supervised Clustering
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    Chapter 10 Consensus of Clusterings Based on High-Order Dissimilarities
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    Chapter 11 Hubness-Based Clustering of High-Dimensional Data
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    Chapter 12 Clustering for Monitoring Distributed Data Streams
Attention for Chapter 10: Consensus of Clusterings Based on High-Order Dissimilarities
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Citations

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Chapter title
Consensus of Clusterings Based on High-Order Dissimilarities
Chapter number 10
Book title
Partitional Clustering Algorithms
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-09259-1_10
Book ISBNs
978-3-31-909258-4, 978-3-31-909259-1
Authors

Helena Aidos, Ana Fred, Aidos, Helena, Fred, Ana

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 40%
Researcher 1 20%
Professor > Associate Professor 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Engineering 2 40%
Unknown 1 20%

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