This unique compendium gives an updated presentation of clustering, one of the most challenging tasks in machine learning. The book provides a unitary presentation of classical and contemporary algorithms ranging from partitional and hierarchical clustering up to density-based clustering, clustering of categorical data, and spectral clustering.Most of the mathematical background is provided in appendices, highlighting algebraic and complexity theory, in order to make this volume as self-contained as possible. A substantial number of exercises and supplements makes this a useful reference textbook for researchers and students.
Contents:
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Preface
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Introduction
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Set-Theoretical Preliminaries
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Dissimilarities, Metrics, and Ultrametrics
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Convexity
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Graphs and Hypergraphs
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Partitional Clustering
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Statistical Approaches to Clustering
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Hierarchical Clustering
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Density-based Clustering
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Categorical Data Clustering
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Spectral Clustering
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Correlation and Consensus Clustering
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Clustering Quality
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Clustering Axiomatization
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Biclustering
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Semi-supervised Clustering
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Appendices:
- Special Functions and Applications
- Linear Algebra
- Linear Programming
- NP Completeness
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Bibliography
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Index
Readership: Researchers, professionals, academics and graduate students in machine learning, data mining and artificial intelligence.
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