Abstract

A methodological and computational framework for centroid-based partitioning cluster analysis using arbitrary distance or similarity measures is presented. The power of highlevel 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. 1

Citations