Abstract: To solve the problem that the local structure information of data samples is not fully utilized and the sensitivity of the algorithm to noise leads to the decline of the clustering effect, this paper proposes the intuitionistic fuzzy local preserving projection least square twin support vector clustering method. Fuzzy scores are assigned based on the distance between samples and centroids and the heterogeneity of the samples, given weights to the samples, and the local geometric structure information of the training sample is fully utilized to provide prior information about the sample neighborhood, which not only reduces the influence of noise and outliers on the performance of the algorithm, but also effectively solves the clustering problem of data. Experiments are conducted on several datasets, and the significance of the proposed algorithm is verified by statistical analysis. These experimental results demonstrate that the proposed algorithm has better robustness and clustering performance than other existing algorithms.

Key words: twin support vector, clustering, locality preserving projection, intuitionistic fuzzy, noise