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OverviewThe 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. Full Product DetailsAuthor: Paolo Giordani , Maria Brigida Ferraro , Francesca MartellaPublisher: Springer Verlag, Singapore Imprint: Springer Verlag, Singapore Edition: 1st ed. 2020 Volume: 1 Weight: 0.705kg ISBN: 9789811305528ISBN 10: 9811305528 Pages: 340 Publication Date: 28 August 2020 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsSection: 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.ReviewsThis 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. (Sebastien Bailly, ISCB News, iscb.info, Issue 71, June, 2021) Author InformationPaolo 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 Tab Content 6Author Website:Countries AvailableAll regions |