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Köp båda 2 för 2291 krThis book is written for anybody who would like to start clustering using R and considers both practical and theoretical aspects. this is an in-depth introduction to clustering analysis considering both the theory and applications in R, with various examples in different fields. More than just an introduction, this would be a very good companion book for researchers to help them understand clustering with R, and to compare the various methods and their applications. (Sbastien Bailly, ISCB News, iscb.info, Issue 71, June, 2021)
Paolo Giordani, Department of Statistical Sciences, Sapienza University of Rome Maria Brigida Ferraro, Department of Statistical Sciences, Sapienza University of Rome Francesca Martella, Department of Statistical Sciences, Sapienza University of Rome
Section: Introduction.- 1.1 Introduction to clustering.- 1.2 R software.- 2. Section: Standard algorithms.- 2.1 Introduction.- 2.2 Distances and dissimilarities.- 2.3 Hierarchical methods.- 2.4 Non-hierarchical methods.- 2.5 Cluster validity.- 3. Section: Fuzzy algorithms.- 3.1 Introduction.- 3.2 Fuzzy K-means.- 3.3 Fuzzy K-medoids.- 3.4 Other fuzzy variants.- 3.5 Cluster validity.- 4. Section: Model-based algorithms.- 4.1 Introduction.- 4.2 Mixture of Gaussian distributions.- 4.3 Mixture of non-Gaussian distributions.- 4.4 Parsimonious mixture models.