A Toolbox for K-Centroids Cluster Analysis
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| Authors | Friedrich Leisch |
| Journal/Conference Name | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
| Paper Category | Other |
| 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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