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Clustering [electronic resource] : theoretical and practical aspects / Dan A. Simovici.

By: Simovici, Dan A.
Material type: materialTypeLabelBookPublisher: Singapore : World Scientific, 2021Description: 1 online resource (884 p.).ISBN: 9789811241208; 9811241201.Subject(s): Cluster analysisGenre/Form: Electronic books.DDC classification: 519.53 Online resources: Access to full text is restricted to subscribers.
Contents:
Introduction -- Set-theoretical preliminaries -- Dissimilarities, metrics, and ultrametrics -- Convexity -- Graphs and hypergraphs -- Partitional clustering -- Statistical approaches to clustering -- Hierarchical clustering -- Density-based clustering -- Categorical data clustering -- Spectral clustering -- Correlation and consensus clustering -- Clustering quality -- Clustering axiomatization -- Biclustering -- Semi-supervised clustering.
Summary: "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."-- Publisher's website.
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Introduction -- Set-theoretical preliminaries -- Dissimilarities, metrics, and ultrametrics -- Convexity -- Graphs and hypergraphs -- Partitional clustering -- Statistical approaches to clustering -- Hierarchical clustering -- Density-based clustering -- Categorical data clustering -- Spectral clustering -- Correlation and consensus clustering -- Clustering quality -- Clustering axiomatization -- Biclustering -- Semi-supervised clustering.

"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."-- Publisher's website.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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