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Unsupervised Learning Algorithms

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Cover of 'Unsupervised Learning Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Anomaly Detection for Data with Spatial Attributes
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    Chapter 2 Anomaly Ranking in a High Dimensional Space: The Unsupervised TreeRank Algorithm
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    Chapter 3 Genetic Algorithms for Subset Selection in Model-Based Clustering
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    Chapter 4 Clustering Evaluation in High-Dimensional Data
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    Chapter 5 Combinatorial Optimization Approaches for Data Clustering
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    Chapter 6 Kernel Spectral Clustering and Applications
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    Chapter 7 Uni- and Multi-Dimensional Clustering Via Bayesian Networks
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    Chapter 8 A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks
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    Chapter 9 A Survey of Constrained Clustering
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    Chapter 10 An Overview of the Use of Clustering for Data Privacy
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    Chapter 11 Nonlinear Clustering: Methods and Applications
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    Chapter 12 Swarm Intelligence-Based Clustering Algorithms: A Survey
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    Chapter 13 Extending Kmeans-Type Algorithms by Integrating Intra-cluster Compactness and Inter-cluster Separation
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    Chapter 14 A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression
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    Chapter 15 Unsupervised Learning in Genome Informatics
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    Chapter 16 The Application of LSA to the Evaluation of Questionnaire Responses
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    Chapter 17 Mining Evolving Patterns in Dynamic Relational Networks
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    Chapter 18 Probabilistically Grounded Unsupervised Training of Neural Networks
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Unsupervised Learning Algorithms
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-24211-8
978-3-31-924209-5, 978-3-31-924211-8

M. Emre Celebi, Kemal Aydin

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 388 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Italy 1 <1%
Brazil 1 <1%
Unknown 384 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 16%
Student > Master 57 15%
Researcher 35 9%
Student > Bachelor 35 9%
Student > Doctoral Student 22 6%
Other 50 13%
Unknown 127 33%
Readers by discipline Count As %
Computer Science 93 24%
Engineering 69 18%
Business, Management and Accounting 9 2%
Biochemistry, Genetics and Molecular Biology 7 2%
Mathematics 7 2%
Other 57 15%
Unknown 146 38%