An Efficient Density-Based Algorithm for Data Clustering

In this paper, we propose a novel density-based clustering method in which we deal with data appearing sequentially. In data mining, a cluster is a high-density region gathering a set of objects which are similar according to a prefixed criterion. For purposes of modelling, we restrict a cluster to be the contour of the region including these objects. The bounded contour function is obtained by applying a B-spline interpolation on the convex hull vertices enclosing the cluster. This procedure, named Cluster Domain Description (CDD), may give a realistic approximation of the cluster area. The clustering process is achieved afterwards with respect to the variation of the internal density of that area. In order to improve performances, a supplementary merge mechanism of evolving clusters is as well proposed. The method is assessed firstly on artificially generated data, and then on data extracted from a chemical system consisting of the Tennessee Eastman Process.