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Deep clustering uses a deep neural network to learn deep feature representation for performing clustering tasks. In this paper, we explored the Deep Clustering Embedded Clustering (DCEC) method, which employs a standart clustering method to get initial weight for the neural model training incorporated 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 based K-Harmonic Means was also introduced in this study to reduce the effect of outliers and noise. We evaluated the performance of these clustering approaches wthin DCEC over benchmark image datasets and the results were better than the baseline.

Exploring centroids initialization within Deep Convolutional Embedded Clustering