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.
The paper presents a new Kernel-based monitoring algorithm compared with statistical process control methods, such as DISSIM and MS-PCA and some other methods widely used in process control applications. The proposed algorithm is a modified version of the well known support vector domain description (SVDD). The last one is commonly used for one-classification problems, named also novelty detection. In this paper, we have used a modified SVDD endowed with useful tools to manage multi-classification problems. The proposed classifier is also able to deal with stationary as well as non-stationary data. The principle is based on the dynamic update of the training set through a recursive deletion/insertion procedure according to adequate rules. In order to reduce the computational complexity and improve the rapidity of convergence, the algorithm considers in each run a limited frame of samples for the training process. To prove its effectiveness, the approach is assessed at first on artificially generated data. Then, we have performed a case study applied on chemical process.
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