Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 51, Issue 2, Pages 526-544Publisher
ELSEVIER
DOI: 10.1016/j.csda.2005.10.006
Keywords
cluster analysis; distance measures; R
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A methodological and computational framework for centroid-based partitioning cluster analysis using arbitrary distance or similarity measures is presented. The power of high-level statistical computing environments like R enables data analysts to easily try out various distance measures with only minimal programming effort. A new variant of centroid neighborhood graphs is introduced which gives insight into the relationships between adjacent clusters. Artificial examples and a case study from marketing research are used to demonstrate the influence of distances measures on partitions and usage of neighborhood graphs. (c) 2005 Elsevier B.V. All rights reserved.
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