# A Hybrid Autoencoder Network for Unsupervised Image Clustering
## 1 Introduction  
Data mining (DM) is the one of the key processes of knowledge discovery in databases (KDD) to transform raw data into interesting information and knowledge [1] and is the main research topic in the field of machine learning and artificial intelligence. The classification of data mining depends on the tasks of the problem; one of the tasks is clustering, which groups similar data into a segment. Here, we focus on image clustering, which is an essential issue in machine learning and computer vision. Hence, the purpose of image clustering is to group images into clusters such that an image is similar to others within a cluster.  
Many statistical methods, e.g., \(k\)-means or DBSCAN, have been used in image clustering. However, these methods have difficulty in handling image data, since images are usually high-dimensional, resulting in the poor performance of these traditional methods [2]. Recently, with the development of deep learning, more neural network models have been developed for image clustering. The most famous one is the autoencoder (AE) network, which firstly pre-trains deep neural networks with unsupervised methods and employs traditional methods, e.g., k-means, for clustering images in post-processing. More recently, several autoencoder-based networks have been proposed--e.g., the convolutional autoencoder (CAE) [3], adversarial autoencoder (AAE) [4], stacked autoencoder (SAE) [5], variational autoencoder (VAE) [6; 7], etc.--and these models have been reported to achieve great success in the fields of supervised and unsupervised learning [8; 9; 10].  
However, as we know of no technique or model which outperforms others in all situations, every model has its specialty to specific tasks or functions. Therefore, although numerous autoencoder-basedmodels have been proposed to learn feature representations from images, the best one depends on the situation. Hence, in this paper, we consider a hybrid model, namely the hybrid autoencoder (BAE), to integrate the advantages of three autoencoders, CAE, AAE, and SAE, to learn low and high-level feature representation. Then, we use the \(k\)-means to cluster images. The concept of hybrid models is not a new idea. For example, [11] integrated AE and density estimation models to take advantage of their different strengths for anomaly detection. [12] proposed a hybrid spatial-temporal autoencoder to detect abnormal events in videos by the integration of a long short-term memory (LSTM) encoder-decoder and the convolutional autoencoder. These results of the hybrid models outperform other state-of-the-art models.  
The differences of the proposed method from others are that first we focus on the task of clustering, and then we integrate different AE-based methods, which were not considered previously. In addition, in our experiment, we use two datasets, MNIST and CIFAR-10, which are famous image datasets, to compare the clustering performance with others. The experiment results indicate the clustering performance of the proposed method is better than that of others with respect to the results of unsupervised clustering accuracy (ACC), normalized mutual information (NMI), and adjusted rand index (ARI).