Sparse K-means clustering algorithm with anchor graph regularization

Abstract

As a classical unsupervised learning method, the K-means algorithm selects the cluster centers randomly and calculates the mean values of the cluster's data points to generate clusters. However, its performance is susceptible to the initial cluster centers and the sparsity of the membership matrix. To overcome these limitations, in this paper, we propose a sparse K-means clustering algorithm with anchor graph regularization (SKM-AGR) for optimizing initial cluster center sensitivity and improving membership matrix sparsity. The main idea is to use the anchor graph regularization (AGR) constrained K-means models, which effectively learn the membership matrix of data points and the membership matrix of anchors. In particular, by constructing an anchor graph, the AGR term not only discovers the internal structure information of data, but also covers the data distribution. Furthermore, an alternating optimization algorithm with fast-converging is adopted to solve the optimization problems of SKM-AGR, and the computational complexity is analyzed. Extensive clustering experiments on several synthetic and benchmark datasets show that the proposed SKM-AGR method performs better than several previous methods in most cases.