关键词: 深度图像聚类, 卷积自编码, 卷积神经网络（CNN）, 深度学习, 聚类

Abstract: To avoid the big characteristic loss of deep convolutional embedded clustering (DCEC) algorithm, especially for complex images, a 17-layer deep network framework is proposed in this paper for unsupervised deep image clustering analysis, where subsampling layer is embedded in encode layers to reduce parameters and prevent overfitting while up-sampling is embedded in decode layers to restore the specific loss by subsampling in encode layers. Combining the loss functions of deep embedded clustering (DEC) and improved deep embedded clustering (IDEC), two deep convolutional autoencoder based algorithms for image clustering analysis are proposed in this paper, named as DEC_DCNN (deep embedded clustering based on deep convolutional neural network) and IDEC_DCNN (improved deep embedded clustering based on deep convolutional neural network) respectively. Adam (adaptive moment estimation) and Mini-Batch SGD (mini-batch stochastic gradient decent) are adopted to optimize parameters for the proposed algorithms. Three typical image datasets are used to test the power of the proposed algorithms. The experimental results demonstrate that the proposed 17-layer deep network framework is very robust and general. The DEC_DCNN and IDEC_DCNN algorithms based on the proposed deep network framework have got higher clustering accuracy (ACC) than that of the available clustering algorithms. The IDEC_DCNN is superior to DEC_DCNN in terms of benchmark metrics including AMI (adjusted mutual information), ARI (adjusted rand index) and ACC, which further demonstrates the advantages of IDEC_DCNN.

Key words: deep image clustering, convolutional autoencoders, convolutional neural network (CNN), deep learning, clustering