Molecular dynamics-like data clustering approach

Based on the molecular kinetic theory, a molecular dynamics-like data clustering approach is proposed in this paper. Clusters are extracted after data points fuse in the iterating space by the dynamical mechanism that is similar to the interacting mechanism between molecules through molecular forces. This approach is to find possible natural clusters without pre-specifying the number of clusters. Compared with 3 other clustering methods (trimmed k-means, JP algorithm and another gravitational model based method), this approach found clusters better than the other 3 methods in the experiments.

Research highlights
► A molecular-kinetic-theory-inspired clustering approach is proposed.
► Clusters are extracted after points fuse by the molecular dynamics-like mechanism.
► It can find possible clusters without pre-specifying the number of clusters.
► It has got good clustering result in experiment compared with other methods like JP.