Gaussian gravitation for cluster ensembles

摘要

Gravity-based clustering methods can effectively distinguish the differences between the data points close to the center of a dataset and those on a class boundary. However, most existing methods are only suitable for processing real-valued data. In this paper, the processing scope of gravitational models is extended to discrete data and then utilized to solve cluster ensemble problems. First, a novel Gaussian gravitational model for cluster ensemble (GGMCE) is proposed in which each base cluster can be transformed into an object with a mass via Newton’s second law of motion and thus extending gravitational models to discrete data. Furthermore, instead of setting hyperparameters for the number of neighbors, Gaussian agency strategy is proposed to explore the final cluster assignment. To validate the capability of the proposed model, extensive experiments on a variety of real-world datasets are conducted and the result demonstrates the superiority of the proposed model over the state-of-the-art models.