An Introduction to Clustering with R (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
350
Utgivningsdatum
2020-10-11
Upplaga
1st ed. 2020
Förlag
Springer Verlag, Singapore
Illustratör/Fotograf
Bibliographie
Illustrationer
10 Tables, color; 10 Illustrations, color; 10 Illustrations, black and white; X, 350 p. 20 illus., 1
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9789811305528
An Introduction to Clustering with R (inbunden)

An Introduction to Clustering with R

Inbunden Engelska, 2020-10-11
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The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. 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. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.
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Övrig information

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

Innehållsförteckning

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.