# Fuzzy Rough C-Mean Based Unsupervised CNN Clustering for Large-Scale Image Data
## 2 Fuzzy Rough C-Mean Based Unsupervised CNN Clustering  
### The Problem of Deep-Learning-Based Clustering  
In recent years, the clustering method with the deep neural network has gained additional interest due to the success of supervised learning [46; 51; 52; 53; 54; 55; 56; 57; 58]. However, in most cases, clustering is handled in the unsupervised form, making its application with deep learning more significant and requiring more modeling effort and theoretical analysis.  
Previously deep learning-based clustering algorithms have not focused on data uncertainty reductions to increase the accuracy and decrease the time complexity.  
[PERSON] et al. [45] proposed the first deep-learning based clustering. Deep Embedded Clustering (DEC) is based on auto-encoders as network architecture followed by conventional K-Means for final clustering. The network model is fine-tuned using the cluster assignment hardening less and the cluster centers are updated.  
[PERSON] et al. [64] also proposed the auto-encoder-based method followed by K-Mean clustering. However, the network is jointly trained using a combination of the representation learning and image clustering.  
[PERSON] et al. [58] proposed an idea almost identical to DEC except for using a conventional auto-encoder. However, for high-dimensional data, the auto-encoder usually cannot learn representation features well compared to CNN-based architectures.  
[PERSON] et al. [47] proposed a convolution neural network with re-running clustering (CNN-RC) method. For clustering, a hierarchical clustering approach is utilized. Concerning the training part, the network is jointly trained, and the cluster is updated in the forward pass, while representation learning is in the backward pass. However, compared the centroid-based clustering, hierarchical clustering devours high computation and memory complexity due to the size of image data which becomes large.  
[PERSON] [46] proposed a CNN with the connection matrix (CNN-CM) method; for clustering K-Mean is utilized. A connection matrix is proposed that enables encouraging in additional side data to help to learn the representation for clustering. In the view of learned features, a full-set K-Mean is then performed to gather all images into their relating clusters. However, when the size of data becomes large, the complexity of full-set K-Mean will increase.  
[PERSON] & [PERSON] [44] proposed clustering CNN to achieve joint clustering and representation learning with feature Drift Compensation for large-scale image data. They extracted the silent features from one of the internal layers of CCNN. At first, initial cluster centroids are assumed from extracted features of randomly picked k samples, and K-Mean is performed on the features extracted from the input dataset to get corresponding cluster labels. Based on the assigned labels and labels predicted by the Softmax layer, the network parameters can be updated. Further, the corresponding cluster centroids are updated by extracted features of the mini-batch. However, the fuzzy modeling achieves many advantages over the non-fuzzy method, such as robustness against uncertainties, vagueness, and overlapping dataset.