Exploring centroids initialization within Deep Convolutional Embedded Clustering - Nogueira, Leonardo; Serapião, Adriane (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2019: Anais do XVI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2019.9307, 2019)
Deep clustering uses a deep neural network to learn deep feature representation for performing clustering tasks. In this paper, we explored the Deep Convolutional Embedded Clustering (DCEC) method, which employs a stan- dart clustering method to get initial weight for the neural model training incor- porated to other clustering methods. The original DCEC uses K-Means with Euclidean distance for the clusters center initialization step. We have applied K-Means with Mahalanobis distance instead of Euclidean distance. In order to improve the DCEC performance, we have included the standart K-Harmonic Means clustering algorithm as well, which tries overcome the dependency of the K-Means performance on the clusters center initialization. The Kernel ba- sed K-Harmonic Means was also introduced in this study to reduce the effect of outliers and noise. We evaluated the performance of these clustering appro- aches within DCEC over benchmark image datasets and the results were better than the baseline.