Energy-based centroid identification and cluster propagation with noise detection

Abstract

Clustering algorithms are used to partition an existing set of objects into groups according to similarity of their attributes. Parametric algorithms for determining initial points (centroids) and subsequent cluster propagation are proposed. The principle of competitive growth of clusters due to the absorption of boundary (contiguous) objects is used. The object is absorbed by that cluster or transferred from an adjacent cluster if it maximizes the total energy of cluster. The remaining objects that have not been clustered are classified as noise. Then the parameter identification problem for the algorithm is considered. Preliminary results on clustering and parameter identification are obtained on several public test data sets.