Abstract: With the advent of the era of big data, the information resource is growing rapidly, and the data are becoming high-dimensional. Traditional clustering methods have a good effect for low-dimensional data, but no longer apply to high-dimensional data. On the basis of existing high-dimensional clustering algorithm, a high-dimensional clustering algorithm based on intelligent optimization SSC-BA is proposed. A novel objective function is designed, which integrates the fuzzy weighting within-cluster compactness and the between-cluster separation. A variant bat algorithm is introduced to calculate the weight matrix, giving the new learning rules. Simulation experiments are made for the proposed algorithm, and other soft subspace clustering algorithm is compared with the test. Experimental results show that the clustering algorithm is suitable for high-dimensional data, and has certain performance advantages compared with other algorithms.

Key words: high-dimensional clustering, weighting matrix, bat algorithm, mutation strategy