00838nam0-2200301 --450 991042956010332120210115112618.097811084259649781108441964pbk20210115d2018----kmuy0itay5050 baengGB 001yyHungry nationfood, famine, and the making of modern IndiaBenjamin Robert SiegelCambridgeCambridge University press2018VI, 280 p.ill.24 cm363.8338.10954954.05954.04Siegel,Benjamin Robert790336ITUNINAREICATUNIMARCBK9910429560103321363.8 SIE 12020/1847FLFBCFLFBCHungry nation1764474UNINA04085nam 22006855 450 991086313510332120250609111603.09789811305535981130553610.1007/978-981-13-0553-5(CKB)4100000011401114(MiAaPQ)EBC6320880(DE-He213)978-981-13-0553-5(PPN)250213184(MiAaPQ)EBC6320711(EXLCZ)99410000001140111420200827d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAn Introduction to Clustering with R /by Paolo Giordani, Maria Brigida Ferraro, Francesca Martella1st ed. 2020.Singapore :Springer Nature Singapore :Imprint: Springer,2020.1 online resource (346 pages)Behaviormetrics: Quantitative Approaches to Human Behavior,2524-4035 ;19789811305528 9811305528 Includes bibliographical references.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.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 interestedin 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.Behaviormetrics: Quantitative Approaches to Human Behavior,2524-4035 ;1StatisticsMathematical statisticsData processingStatisticsBiometryStatistical Theory and MethodsStatistics and ComputingApplied StatisticsBiostatisticsStatistics.Mathematical statisticsData processing.Statistics.Biometry.Statistical Theory and Methods.Statistics and Computing.Applied Statistics.Biostatistics.519.53Giordani Paolo(Writer on statistics.authttp://id.loc.gov/vocabulary/relators/aut178971Ferraro Maria Brigidaauthttp://id.loc.gov/vocabulary/relators/autMartella Francescaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910863135103321An Introduction to Clustering with R4165771UNINA