Evolutionary clustering with DBSCAN

Clustering algorithms have been used in the field of data mining for such a long time. With the accumulation of the online data sets, studies on cluster evolution were carried out so as to decrease noise and maintain continuity of clustering results. A number of evolutionary clustering algorithms have been proposed, such as the evolutionary K-means and Spectral clustering, but none of them were engaged to solving the density-based clustering problem. In this paper, we initially present an evolutionary clustering algorithm with DBSCAN (density-based spatial clustering of applications with noise), which is on the basis of temporal smoothness penalty framework. We conduct the evaluations of our framework both on the random Gaussian dataset and the classical DBSCAN dataset. Compared with the other similar evolutionary clustering algorithms, such as the evolutionary K-means clustering, our method can not only resist to the noise, but also distinguish the clusters with arbitrary shapes during the evolution process.