A Toolbox for K-Centroids Cluster Analysis

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Authors Friedrich Leisch
Paper Category
Paper Abstract 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.
Date of publication 2006
Code Programming Language R

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