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[Page no. 15] "clustering models are designed for speciﬁc data sets. Complex data from different sources and forms bring more uncertainties and challenges to clustering. At present, deep learning and graph learning are needed to solve complex data processing problems. • Model efﬁciency Deep clustering algorithm requires a large number of samples for training. Therefore, in small sample data sets, deep clustering is prone to overﬁtting, which leads to the decrease of clustering effect and the reduction of the generalization performance of the model. On the other hand, the deep clustering algorithm with large-scale data has high computational complexity, so the model structure optimization and model compression technology can be adopted to reduce the computational load of the model and improve the efﬁciency in practical application conditions. • Fusion of multi-view data In practical application scenarios, clustering is often not just with a single image information, but also available text and voice information. However, most of the current deep clustering algorithms can only use one kind of information and can not make good use of the existing information. The subsequent research can consider to fully integrate the information of two or more views and make full use of the consistency and complementarity of data of different views to improve the clustering effect. Furthermore, how to combine features of different views while ﬁltering noise to ensure better view quality needs to be solved. • Deep clustering based on graph learning In reality, a large number of data sets are stored in the form of graph structures. Graph structure can represent the structural association information between sample points. How to effectively use the structural information is particularly important to improve the clustering performance. Whether it is a single-view deep clustering or a relatively wide application of multi-view deep clustering, existing clustering methods based on graph"

[Page no. 15] "this net relies on the quality of the generated images too much. Although the classiﬁer can guarantee the invariant information of classes, it is also hard to perform on complex images. Pixel-level trans- ferring and feature-level transferring are not going against each other, as pixel-level can transfer visual features and feature-level transferring can transfer the nature information of the instances. Cycle-Consistent adversarial domain adaptation (CyCADA) [254] adapts representations at both the pixel-level and feature-level while enforcing semantic consistency. The authors enforce both structural and semantic consistency during adaptation using a cycle-consistency loss and semantics losses based on a particular visual recognition task. The semantics losses both guide the overall representation to be discriminative and enforce semantic consistency before and after mapping between domains. Except for GAN, adopting data augmentation to transfer learning can also use traditional ways. [260] provides the efﬁciency to make data augmentation in the target domain even it is unlabeled. It adds self- supervised tasks to target data and shows good performance. More important is that this skill can be combined with other domain adaptation methods such as CyCADA and DAN. 7 FUTURE DIRECTIONS OF DEEP CLUSTERING Based on the aforementioned literature review and analysis, deep clustering has been applied to several domains, and we attach importance to several aspects worth studying further: • Theoretical exploration Although remarkable clustering performance has been achieved by designing even more sophisticated deep clustering pipelines for speciﬁc problem-solving needs, there is still no reliable theoretical analysis on how to qualitatively analyze the in- ﬂuence of feature extraction and clustering loss on ﬁnal clustering. So, exploring the theoretical basis of deep clustering optimization is of great signiﬁcance for guiding further research in this ﬁeld. • Massive complex data processing Due to the complexity brought by massive data, most of the existing deep"

[Page no. 16] "learning still have some problems, such as the graph structure information is not fully utilized, the differences and importance of different views are not fully considered. Therefore, the effective analysis of complex graph structure information, especially the rational use of graph structure information to complete the clustering task, needs further exploration. 8 SUMMARY OF DEEP CLUSTERING METHODS In this paper, we introduce recent advances in the ﬁeld of deep clustering. This is mainly kind of data structures: single- view, semi-supervised, multi-view, and transfer learning. Single- view methods are the most important part of our survey, which inherits the problem settings of traditional clustering methods. We introduce them from the network they are based on. Among these networks, DAE-based methods and DNN-based methods are proposed earlier but limited with their poor performance in a real dataset. Compared to DAE-based and CNN-based methods, VAE-based and GAN-based methods attract attention in recent years for their strong feature extraction and sample generation power. Graph neural network is one of the most popular networks recently, especially in community discovery problems. So we  16 also summarize the GNN-based clustering methods. With the development of the internet, the data for clustering have different application scenarios, so we summarize some clustering methods which have different problem settings. Semi-supervised clustering methods cluster the data with constraints that can be developed from single-view clustering methods by adding a constraints loss. Multi-view clustering methods use the information of different views as a supplement. It has been used widely in both traditional neural networks and graph neural networks. Transfer learning can transfer the knowledge of a labeled domain to an unlabeled do- main. We introduce clustering methods based on transfer learning with two types of networks: DNN and GAN. DNN-based methods focus on the measurement strategy of two domains. GAN-based methods"

[Page no. 1] "et al. [29] classify deep clustering methods from the perspective of different deep networks. Nutakki et al. [30] divide deep single-view clustering methods into three categories according to their training strategies: multi-step sequential deep clustering, joint deep clustering, and closed-loop multi-step deep clustering. Zhou et al. [31] categorize deep single-view clustering methods by the interaction way between feature learning and clustering modules. But in the real world, the datasets for clustering are always associated, e.g., the taste for reading is correlated with the taste for a movie, and the side face and full-face from the same person should be labeled the same. For these data, deep clustering methods based on semi-supervised learning, multi-view learning, and transfer learning have also made signiﬁcant progress. Unfortunately, existing reviews do not discuss them too much. Therefore, it is important to classify deep clustering from the perspective of data sources and initial conditions. In this survey, we summarize the deep clustering from the perspective of initial settings of data combined with deep learning methodology. We introduce the newest progress of deep clustering from the perspective of network and data structure as shown in Fig. 1. Speciﬁcally, we organize the deep clustering methods into the following four categories: • Deep single-view clustering For conventional clustering tasks, it is often assumed that the data are of the same form and structure, as known as single- view or single-modal data. The extraction of representations for these data by deep neural networks (DNNs) is a signiﬁcant characteristic of deep clustering. However, what is more note- worthy is the different applied deep learning techniques, which are highly correlated with the structure of DNNs. To compare the technical route of speciﬁc DNNs, we divide those algorithms into ﬁve categories: deep autoencoder (DAE) based deep clustering, arXiv:2210.04142v1 [cs.LG] 9 Oct 2022"

[Page no. 1] "1 Deep Clustering: A Comprehensive Survey Yazhou Ren, Member, IEEE, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Fellow, IEEE, Lifang He, Member, IEEE Abstract—Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view ﬁelds and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different ﬁelds of deep clustering. Index Terms—Deep clustering; semi-supervised clustering; multi-view clustering; transfer learning ! 1 INTRODUCTION W ITH the development of online media, abundant data with high complexity can be gathered easily. Through pinpoint analysis of these data, we can dig the value out and use these conclusions in many ﬁelds, such as face recognition [1], [2], sentiment analysis [3], [4], intelligent manufacturing [5], [6], etc. A model which can be used to classify the data with different labels is the base of many applications. For labeled data, it is taken granted to use the labels as the most important information as a guide. For unlabeled data, ﬁnding a quantiﬁable objective as the guide of the model-building process is the key question of clustering. Over the"

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