A Deep Clustering Algorithm based on Gaussian Mixture Model

Abstract

Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm.