Abstract
We present a method to cluster multivariate data according to their density (or any other target function): all observations lying within one “basin” or sitting on the slopes of one “mountain” are assigned to one cluster. The method exploits a neighborhood structure given by the Delaunay triangulation or a k-nearest neighbor graph, and each cluster is given in terms of a basin spanning tree. The root of each basin spanning tree corresponds to a local density maximum, and the trees can be used for simplified representation and visualization of the observations. We compare the accuracy and speed of different approximations, apply the method to real-world data sets and compare its computational complexity to published algorithms.
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Hader, S., Hamprecht, F.A. (2003). Efficient Density Clustering Using Basin Spanning Trees. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_5
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DOI: https://doi.org/10.1007/978-3-642-18991-3_5
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