Choosing DBSCAN parameters automatically using differential evolution

Over the last several years, DBSCAN (Density-Based Spatial Clustering
of Applications with Noise) has been widely applied in
many areas of science due to its simplicity, robustness against
noise (outlier) and ability to discover clusters of arbitrary shapes.
However, DBSCAN algorithm requires two initial input parameters,
namely Eps (the radius of the cluster) and MinPts (the
minimum data objects required inside the cluster) which both
have a significant influence on the clustering results. Hence, DBSCAN
is sensitive to its input parameters and it is hard to determine
them a priori. This paper presents an efficient and effective
hybrid clustering method, named BDE-DBSCAN, that
combines Binary Differential Evolution and DBSCAN algorithm
to simultaneously quickly and automatically specify appropriate
parameter values for Eps and MinPts. Since the Eps parameter
can largely degrades the efficiency of the DBSCAN algorithm,
the combination of an analytical way for estimating Eps
and Tournament Selection (TS) method is also employed. Experimental
results indicate the proposed method is precise in determining
appropriate input parameters of DBSCAN algorithm.