Transfer learning-assisted multi-objective evolutionary clustering framework with decomposition for high-dimensional data

Abstract

Although multi-objective evolutionary subspace clustering approaches have shown promise in handling high-dimensional datasets, their performance is restricted by two main drawbacks. First, their local search strategies have not been well investigated. Second, while exploring the search space, they neglect the useful knowledge from previously solved problems. To tackle these issues, this paper proposes a transfer learning-assisted multi-objective evolutionary clustering framework with decomposition. Firstly, we provide a decomposition-based local search strategy. To capture a comprehensive data structure, this strategy updates the weights of features by considering both the within-class compactness and between-class separation, and spontaneously balances the two properties. Secondly, we develop a knowledge transfer strategy. By transferring search experience from a previously solved clustering problem, the strategy improves the search efficiency, consequently enhances the clustering accuracy of the current problem. It has a closed-form solution and can transfer knowledge across both homogeneous and heterogeneous problems from either different or the same domains. Finally, we conduct an extensive experimental study on the framework by comparing with six representative subspace clustering approaches on a wide range of benchmarks and real-world applications. Results demonstrate the superiority of our framework.