A Comparison of Knee Strategies for Hierarchical Spatial Clustering

  • Brian J. RossEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)


A comparative study of the performance of knee detection approaches for the hierarchical clustering of 2D spatial data is undertaken. Knee detection is usually performed on the dendogram generated during cluster generation. For many problems, the knee is a natural indication of the ideal or optimal number of clusters for the given problem. This research compares the performance of various knee strategies on different spatial datasets. Two hierarchical clustering algorithms, single linkage and group average, are considered. Besides determining knees using conventional cluster distances, we also explore alternative metrics such as average global medoid and centroid distances, and F score metrics. Results show that knee determination is difficult and problem dependent.


Knee Hierarchical clustering Spatial clustering 



This research was supported by NSERC Discovery Grant RGPIN-2016-03653.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceBrock UniversitySt. CatharinesCanada

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