Sequential Sparse Adaptive Possibilistic Clustering
Possibilistic clustering algorithms have attracted considerable attention, during the last two decades. A major issue affecting the performance of these algorithms is that they involve certain parameters that need to be estimated accurately beforehand and remain fixed during their execution. Recently, a possibilistic clustering scheme has been proposed that allows the adaptation of these parameters and imposes sparsity in the sense that it forces the data points to “belong” to only a few (or even none) clusters. The algorithm does not require prior knowledge of the exact number of clusters but, rather, only a crude overestimate of it. However, it requires the estimation of two additional parameters. In this paper, a sequential version of this scheme is proposed, which possesses all the advantages of its ancestor and in addition, it requires the (crude) estimation of just a single parameter. Simulation results are provided that show the effectiveness of the proposed algorithm.
Keywordspossibilistic clustering parameter adaptivity sparsity sequential processing k-means fuzzy c-means
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