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SpringerBriefs in Computer Science

Understanding High-Dimensional Spaces

Authors: Skillicorn, David B.

  • High-dimensional spaces arise naturally as a way of modelling datasets with many attributes
  • Author suggests new ways of thinking about high-dimensional spaces using two models
  • Valuable for practitioners, graduate students and researchers
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eBook $29.99
price for USA
  • ISBN 978-3-642-33398-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Softcover $39.95
price for USA
  • ISBN 978-3-642-33397-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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 work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. 

There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets are large and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. 

The book will be of value to practitioners, graduate students and researchers.

About the authors

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.

Reviews

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)


Table of contents (9 chapters)

Buy this book

eBook $29.99
price for USA
  • ISBN 978-3-642-33398-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Softcover $39.95
price for USA
  • ISBN 978-3-642-33397-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Understanding High-Dimensional Spaces
Authors
Series Title
SpringerBriefs in Computer Science
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
The Author
eBook ISBN
978-3-642-33398-9
DOI
10.1007/978-3-642-33398-9
Softcover ISBN
978-3-642-33397-2
Series ISSN
2191-5768
Edition Number
1
Number of Pages
IX, 108
Number of Illustrations and Tables
29 b/w illustrations
Topics