Introduction to Clustering with R

Paolo Giordani, Department of Statistical Sciences, Sapienza University of RomeMaria Brigida Ferraro, Department of Statistical Sciences, Sapienza University of RomeFrancesca Martella, Department of Statistical Sciences, Sapienza University of Rome

• Section: Introduction.- 1.1Introduction to clustering.- 1.2R software.- 2.Section: Standard algorithms.- 2.1Introduction.- 2.2Distances and dissimilarities.- 2.3Hierarchical methods.- 2.4Non-hierarchical methods.- 2.5Cluster validity.- 3.Section: Fuzzy algorithms.- 3.1Introduction.- 3.2Fuzzy K-means.- 3.3Fuzzy K-medoids.- 3.4Other fuzzy variants.- 3.5Cluster validity.- 4.Section: Model-based algorithms.- 4.1Introduction.- 4.2 Mixture of Gaussian distributions.- 4.3Mixture of non-Gaussian distributions.- 4.4 Parsimonious mixture models.

“This 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.” (Sébastien Bailly, ISCB News, iscb.info, Issue 71, June, 2021)