Advertisement
Published in:
2005 | OriginalPaper | Chapter
Correspondence Clustering of Dortmund City Districts
Author : Stefanie Scheid
Published in: Classification — the Ubiquitous Challenge
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
We combine correspondence analysis (CA) and
K
-means clustering to divide Dortmund's districts into groups that are associated to particular variables and thus represent a social cluster. CA visualizes associations between rows and columns of a frequency matrix and can be used for dimension reduction. Based on the first three dimensions after CA mapping we find a stable partition into five clusters. We further identify variables that are highly associated with the cluster centroids and thus represent a cluster's social condition.