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.
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.
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)
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