Clustering methodology for symbolic data / / Lynne Billard (University of Georgia), Edwin Diday (Universite de Paris IX--Dauphine)

• Record Nr.: UNINA9910814409603321
• Autore: Billard L (Lynne), <1943->
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , [2020]; ©2020
• ISBN: 1-119-01039-X; 1-119-01038-1; 1-119-01040-3
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (351 pages)
• Disciplina: 519.53
• Soggetti: Cluster analysis; Multivariate analysis
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction -- Symbolic data, basics -- Dissimilarity, similarity, and distance measures -- Dissimilarity, similarity, and distance measures, modal data -- General clustering techniques -- Partitioning techniques -- Divisive hierarchical clustering -- Agglomerative hierarchical clustering.
• Sommario/riassunto: Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new ; classification methodologies for histogram valued data reaching across many fields in data science Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis Features very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued data Considers classification models by dynamical clustering Features a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.