Global and local clustering with kNN and local PCA

Abstract

This paper proposes a new clustering method that combines the k Near Neighbor (k NN) method and the local Principal Component Analysis (PCA) to consider the global and local information of data points for clustering. Specifically, we propose firstly preserving the local information of samples using the k NN method to obtain a neighborhood subset and a covariance matrix for each data point, and then preserving the global information of the data by conducting the local PCA on each covariance matrix to obtain a binary affinity matrix of the data. Furthermore, our method conducts clustering on the resulting affinity matrix without the assignment of clustering number. Experimental analysis on 8 UCI benchmark datasets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance.