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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Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Skillicorn, David B. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Σειρά:SpringerBriefs in Computer Science,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.