DenMG: Density-Based Member Generation for Ensemble Clustering.

Ensemble clustering is a popular approach for identifying clusters in data, which combines results from multiple clustering algorithms to obtain more accurate and robust clusters. However, the performance of ensemble clustering algorithms greatly depends on the quality of its members. Based on this observation, this paper proposes a density-based member generation (DenMG) algorithm that selects ensemble members by considering the distribution consistency. DenMG has two main components, which split sample points from a heterocluster and merge sample points to form a homocluster, respectively. The first component estimates two probability density functions ( p.d.f.s) based on an heterocluster's sample points, and represents them using a Gaussian distribution and a Gaussian mixture model. If random numbers generated by these two p.d.f.s are deemed to have different probability distributions, the heterocluster is split into smaller clusters. The second component merges clusters that have high neighborhood densities into a homocluster. This is done using an opposite-oriented criterion that measures neighborhood density. A series of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed algorithm can generate high quality ensemble members and as a result yield better clustering than five state-of-the-art ensemble clustering algorithms.