This handbook 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. It covers the traditional core approaches to cluster analysis, along with additional approaches, such as constrained and semi-supervised clustering. It also explores other relevant issues, such as cluster validation.
This handbook 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. It covers the traditional core approaches to cluster analysis, along with additional approaches, such as constrained and semi-supervised clustering. It also explores other relevant issues, such as cluster validation.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Christian Hennig is a senior lecturer in the Department of Statistical Science at University College London. Dr. Hennig is currently secretary of the International Federation of Classification Societies and associate editor of Statistics and Computing, Computational Statistics and Data Analysis, Advances in Data Analysis and Classification, and Statistical Methods and Applications. His main research interests are cluster analysis, philosophy of statistics, robust statistics, multivariate analysis, data visualization, and model selection. Marina Meila is a professor of statistics at the University of Washington. She earned a PhD in computer science and electrical engineering from the Massachusetts Institute of Technology. Her long-term interest is in machine learning and reasoning in uncertainty and how these can be performed efficiently on large, complex data sets. Fionn Murtagh is a professor of data science at University of Derby and Goldsmiths University of London. Dr. Murtagh is a fellow of the International Association for Pattern Recognition, a fellow of the British Computer Society, an elected member of the Royal Irish Academy and Academia Europaea, a member of the editorial boards of many journals, and editor-in-chief of the Computer Journal. His research interests encompass data science and big data analytics. Roberto Rocci is a professor of statistics in the Department of Economics and Finance at the University of Rome Tor Vergata. Dr. Rocci is associate editor of the Statistical Methods and Applications Journal and board member of the SIS-CLassification and Data Analysis Group (SIS-CLADAG). His research interests include cluster analysis, mixture models, and latent variable models.
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