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Understanding High-Dimensional Spaces

BuchKartoniert, Paperback
108 Seiten
Englisch
Springererschienen am27.09.20122012
High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
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Produkt

KlappentextHigh-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values.
ZusammenfassungThis book proposes new ways of thinking about high-dimensional spaces using two models: the skeleton that relates the clusters to one another, and the boundaries in empty space that provide new perspectives on outliers and on outlying regions.
Details
ISBN/GTIN978-3-642-33397-2
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2012
Erscheinungsdatum27.09.2012
Auflage2012
ReiheSpringerBriefs in Computer Science
Seiten108 Seiten
SpracheEnglisch
Gewicht198 g
IllustrationenIX, 108 p. 29 illus.
Artikel-Nr.18359504

Inhalt/Kritik

Inhaltsverzeichnis
Introduction.- Basic Structure of High-Dimensional Spaces.- Algorithms.- Spaces with a Single Center.- Spaces with Multiple Clusters.- Representation by Graphs.- Using Models of High-Dimensional Spaces.- Including Contextual Information.- Conclusions.- Index.- References.mehr
Kritik
From the reviews:
Selected by Computing Reviews as one of the Best Reviews & Notable Books of 2013
"This brief eight-chapter book seeks to provide the reader with the tools to perform analysis of high-dimensional datasets and spaces. ... book follows a very gentle trajectory. ... This gentle approach makes the book accessible to those unfamiliar with the field of data analysis. ... a good introduction to the area of cluster analysis of high-dimensional data. ... a useful addition to the existing literature on cluster analysis in high-dimensional spaces by providing a starting point for those wanting an initial grounding in the area." (Harry Strange, Computing Reviews, May, 2013)mehr

Schlagworte

Autor

Prof. David B. Skillicorn is a professor in the School of Computing at Queen's University in Kingston, Ontario; he is also an adjunct professor in the Mathematics and Computer Science Department of the Royal Military College of Canada. His research interests include data mining, knowledge discovery, machine learning, parallel and distributed computing, intelligence and security informatics, and collaborative research.