Improvement of Subspace Clustering Based on Differential Evolution

The Subspace Clustering Algorithm Based on Differential Evolution (DESC) performs well among the subspace clustering algorithms of global search. But the fixed number of iterations used may cause the algorithm to terminate when it does not converge or lead to overfitting. The improved algorithm proposed in this paper replaces the mutation operators of the DESC and uses the smoothness of the change in the optimal value of the objective function as the basis of judging convergence –– so that the algorithm can adaptively determine the termination timing and solve the above problems to a large extent. The experimental results show clearly that the proposed algorithm can terminate in the appropriate number of iterations in most cases and has certain advantages over the original algorithm in clustering accuracy and running time.