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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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The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Italy 1 <1%
Brazil 1 <1%
Unknown 116 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 28%
Student > Master 22 18%
Researcher 13 11%
Student > Bachelor 12 10%
Student > Doctoral Student 8 7%
Other 19 16%
Unknown 12 10%
Readers by discipline Count As %
Computer Science 47 39%
Engineering 31 26%
Mathematics 4 3%
Agricultural and Biological Sciences 3 3%
Economics, Econometrics and Finance 3 3%
Other 14 12%
Unknown 18 15%