Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
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 arelarge 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.
Similar content being viewed by others
Keywords
Table of contents (9 chapters)
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)
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Understanding High-Dimensional Spaces
Authors: David B. Skillicorn
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-642-33398-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author 2012
Softcover ISBN: 978-3-642-33397-2Published: 27 September 2012
eBook ISBN: 978-3-642-33398-9Published: 24 September 2012
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: IX, 108
Number of Illustrations: 29 b/w illustrations
Topics: Information Systems and Communication Service, Data Structures and Information Theory, Artificial Intelligence, Systems and Data Security, e-Commerce/e-business