A robust EM clustering algorithm for Gaussian mixture models - Yang, Miin-Shen; Lai, Chien-Yo; Lin, Chih-Ying (Pattern recognition, 2012)
Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number of its components needs to be given a priori. To resolve these drawbacks of the EM, we develop a robust EM clustering algorithm for Gaussian mixture models, first creating a new way to solve these initialization problems. We then construct a schema to automatically obtain an optimal number of clusters. Therefore, the proposed robust EM algorithm is robust to initialization and also different cluster volumes with automatically obtaining an optimal number of clusters. Some experimental examples are used to compare our robust EM algorithm with existing clustering methods. The results demonstrate the superiority and usefulness of our proposed method. ► We propose a robust EM clustering algorithm for Gaussian mixture models. ► We create a new way to solve these initialization problems of the EM algorithm. ► We construct a schema to automatically obtain an optimal number of clusters. ► The proposed robust EM algorithm is robust to initialization, cluster number, and different cluster volumes.