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dc.contributor.authorHennig, Christian
dc.contributor.authorMeila, Marina
dc.contributor.authorMurtagh, Fionn
dc.contributor.authorRocci, Roberto
dc.date.accessioned2016-11-10T13:01:12Z
dc.date.available2016-11-10T13:01:12Z
dc.date.issued2015-12-01
dc.identifier.citationHennig, C, Meilă, M, Murtagh, F, & Rocci, R. (2016), 'Handbook Of Cluster Analysis', Boca Raton : CRC Pressen
dc.identifier.isbn9781466551886
dc.identifier.urihttp://hdl.handle.net/10545/620803
dc.description.abstractHandbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.
dc.language.isoenen
dc.publisherCRC Pressen
dc.relation.ispartofseriesChapman & Hall/CRC Handbooks of Modern Statistical Methodsen
dc.relation.urlhttps://www.crcpress.com/Handbook-of-Cluster-Analysis/Hennig-Meila-Murtagh-Rocci/p/book/9781466551886en
dc.subjectMultivariate data analysisen
dc.subjectUnsupervised classificationen
dc.subjectData miningen
dc.subjectData analysisen
dc.subjectVisualisationen
dc.titleHandbook of cluster analysisen
dc.typeBooken
dc.contributor.departmentUniversity College Londonen
dc.contributor.departmentUniversity of Washingtonen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentUniversity of Rome Tor Vergataen
html.description.abstractHandbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.


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