Understanding High-Dimensional Spaces

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. Such spaces are not easy to wo...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Skillicorn, David B. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Colección:SpringerBriefs in Computer Science,
Temas:
Computer networks .
Data structures (Computer science).
Information theory.
Artificial intelligence.
Data protection.
Electronic commerce.
Computer Communication Networks.
Data Structures and Information Theory.
Artificial Intelligence.
Data and Information Security.
e-Commerce and e-Business.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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