Density ratio based clustering for discovering clusters with varying densities
Density based clustering algorithms are able to identify clusters of arbitrary shapes and sizes in a dataset which contains noise. It is well known that most of these algorithms, which use a global density threshold, have difficulty identifying all clusters in a dataset having clusters of greatly varying densities. This paper identifies and analyses the condition under which density based clustering algorithms fail in this scenario. It proposes a density ratio based method to overcome this weakness, and reveals that it can be implemented in two approaches. One approach is to modify a density based clustering algorithm to do density ratio based clustering by using its density estimator to compute density ratio. The other approach involves rescaling the given dataset only. An existing density based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. We provide an empirical evaluation using DBSCAN, OPTICS and SNN to show the effectiveness of these two approaches. HighlightsAnalyse a key weakness of density based clustering algorithms.Introduce two approaches based on density ratio to overcome this weakness.ReCon converts an existing density estimator to a density ratio estimator.ReScale transforms a dataset by an adaptive scaling based on density ratio.ReCon and ReScale approaches improve three density based clustering algorithms.
Pattern Recognit.