Newtonian clustering: An approach based on molecular dynamics and global optimization

13 years 3 months ago

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Given a data set, a dynamical procedure is applied to the data points in order to shrink and separate, possibly overlapping clusters. Namely, Newton’s equations of motion are employed to concentrate the data points around their cluster centers, using an attractive potential, constructed specially for this purpose. During this process, important information is gathered concerning the spread of each cluster. In succession this information is used to create an objective function that maps each cluster to a local maximum. Global optimization is then used to retrieve the positions of the maxima that correspond to the locations of the cluster centers. Further reﬁnement is achieved by applying the EM-algorithm to a Gaussian mixture model whose construction and initialization is based on the acquired information. To assess the eﬀectiveness of our method, we have conducted experiments on a plethora of benchmark data sets. In addition we have compared its performance against four clusteri...

Konstantinos Blekas, Isaac E. Lagaris

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