Benchmarking clustering methods
Cluster analysis is widely used for finding groups in data. There are many different clustering methods, developed and used in many different research disciplines. When applied to the same data two clustering methods will generally produce different groupings. There is generally no best grouping and different groupings of the same data may be meaningful. Furthermore, applying cluster analysis requires making various decisions, e.g. selecting a clustering method, distance measure and the number of clusters. Guidelines on how to make these decisions are limited. In my research I study properties of clustering methods. Useful insights can be obtained by systematically varying choice options for a particular clustering method, and by comparing different methods.
|Last modified:||05 September 2019 10.36 a.m.|