Introduction to Clustering with R

Inbunden, Engelska, 2020

Del 1 i serien Behaviormetrics: Quantitative Approaches to Human Behavior

1 800 kr

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Beskrivning

The purpose of this book is to thoroughly prepare the reader for applied research in clustering. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses.

Produktinformation

Utgivningsdatum:2020-08-28
Mått:155 x 235 x 25 mm
Vikt:705 g
Format:Inbunden
Språk:Engelska
Serie:Behaviormetrics: Quantitative Approaches to Human Behavior
Antal sidor:340
Upplaga:20001
Förlag:Springer Verlag, Singapore
ISBN:9789811305528

Utforska kategorier

Mer om författaren

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

Recensioner i media

“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)

Innehållsförteckning

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.