Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Understanding High-Dimensional SpacesConclusions

Understanding High-Dimensional Spaces: Conclusions [Clustering is the process of understanding the structure implicit in a dataset, as a way of understanding more deeply the system that the data describes. This is an inherently messy process, because of the ambiguity of what is meant by “understanding”. It is also a complex process, because of the properties of significant real-world systems, and so the properties of the data about them.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Understanding High-Dimensional SpacesConclusions

Download PDF
2 pages

Loading next page...
 
/lp/springer-journals/understanding-high-dimensional-spaces-conclusions-ShObna00Iu

References (0)

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Springer Berlin Heidelberg
Copyright
© The Author 2012
ISBN
978-3-642-33397-2
Pages
99 –101
DOI
10.1007/978-3-642-33398-9_9
Publisher site
See Chapter on Publisher Site

Abstract

[Clustering is the process of understanding the structure implicit in a dataset, as a way of understanding more deeply the system that the data describes. This is an inherently messy process, because of the ambiguity of what is meant by “understanding”. It is also a complex process, because of the properties of significant real-world systems, and so the properties of the data about them.]

Published: Sep 25, 2012

Keywords: Cluster Algorithm; Minimal Span Tree; Empty Space; Outlier Detection; Global Information

There are no references for this article.