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Meanshift clustering is a well established algorithm that has been applied successfully in image processing and computer vision. Cluster centers are derived by local mode seeking identifying maxima in the normalized density of the data set. Recently, quantum clustering that highly resembles mean shift clustering has been proposed for analyzing microarray expression data. Quantum clustering is based on physical intuition derived from quantum mechanics. By an iterative process using a gradient descent procedure, the potential energy V belonging to the Hamiltonian of the time-indepedent Schrodinger equation develops minima that are identified with cluster centers. The analogies between the wavefunction in quantum clustering and the multivariate kernel density estimator in meanshift clustering are leading to closely related formulations. However, the approach towards the minima of the potential in quantum clustering needs to be performed unrelatedly to the formulation, by gradient descent steps. In contrast, in meanshift clustering the approach towards the maxima of the normalized density is performed by the meanshift vector that is derived by the formulation of the methodology. It points towards the direction of the maximum increase in the underlying density. Based on these observations, we propose implementing meanshift clustering to improve the efficiency of local mode seeking in analyzing expression data.