Multi-objective evolutionary clustering with complex networks

Evolutionary clustering (EC) refers to the applications of evolutionary optimization algorithms such as genetic algorithm to data clustering. Although multi-objective evolutionary clustering algorithms were proposed to simultaneously consider different cluster properties such as compactness and separation, these techniques usually suffer from a reasonable initial population and a pre-defined number of clusters. Besides, the effectiveness of evolutionary operators is decreased in dealing with the clustering problem. On the other side, complex networks play an essential role in different fields of machine learning. In a complex network, points are considered as nodes, and the dataset is shown as a connected weighted graph. Also, complex networks tend to present a modular structure. This paper applies two concepts of complex networks including node centrality and community modularity to introduce a novel multi-objective evolutionary clustering. The proposed centrality modularity-based multi-objective evolutionary clustering (CMMOEC) takes the advantage of nodes similarity to find the best initial population of clustering solutions and provide new structural-based modularity to determine the optimal number of clusters automatically. Moreover, the proposed modularity is used to design a new recombination and mutation operator so that it generates offspring solutions that satisfy more diversity. Experiments carried out on several artificial and real-world datasets with different structures. The performance of the proposed algorithm is evaluated by the Adjusted Rand Index (ARI). Simulation results indicate that the proposed algorithm satisfies better performance in comparison to traditional methods.

Automated summary

This paper introduces a novel multi-objective evolutionary clustering algorithm based on centrality modularity, which uses node similarity to determine the optimal initial population and structural modularity to automatically determine the optimal number of clusters. Experimental results show that the proposed algorithm outperforms traditional methods in terms of performance.