Clustering Methodology for Symbolic Data
Billard, Lynne; Diday, Edwin (2019), Clustering Methodology for Symbolic Data, John Wiley & Sons, Ltd
Type
OuvrageDate
2019Publisher
John Wiley & Sons, Ltd
ISBN
9780470713938
Metadata
Show full item recordAuthor(s)
Billard, LynneDepartment of Statistics
Diday, Edwin
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
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 dataThis 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.Subjects / Keywords
symbolic dataRelated items
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Diday, Edwin; Billard, Lynne (2006-01) Chapitre d'ouvrage
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Billard, Lynne; Diday, Edwin (2003) Article accepté pour publication ou publié
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Billard, Lynne; Diday, Edwin (2004) Document de travail / Working paper
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Cury, Alexandre; Crémona, Christian; Diday, Edwin (2009) Communication / Conférence
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Quantin, Catherine; Billard, Lynne; Touati, Myriam; Andreu, N.; Cotin, Y.; Zeller, Manfred; Afonso, Filipe; Battaglia, G.; Seck, Djamal; Le Teuff, G.; Diday, Edwin (2011) Article accepté pour publication ou publié