# Deep Clustering: A Comprehensive Survey

**Authors:** Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He  
**arXiv:** [2210.04142](https://arxiv.org/abs/2210.04142)  
**Date:** 2022-10-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.04142v1/

## Key points

- Cluster analysis is a crucial technique in machine learning and data mining
- Learning a good data representation is important for successful clustering algorithms
- Deep clustering, which uses deep neural networks, has gained significant attention in various clustering tasks
- Existing surveys on deep clustering have focused on single-view fields and network architectures, neglecting complex application scenarios
- This paper presents a comprehensive survey that explores deep clustering from the perspective of different data sources
- The survey categorizes deep clustering methods into four categories: traditional single-view, semi-supervised, multi-view, and transfer clustering
- The authors provide insights into methodology, prior knowledge utilization, and architectural aspects for each category
- Open challenges and potential future opportunities in deep clustering are discussed
- The survey aims to bridge the gap between existing research by providing a broader understanding of deep clustering in diverse application scenarios.

## Summary

In the field of machine learning and data mining, cluster analysis is a crucial technique that plays an indispensable role. One of the key factors in successful clustering algorithms is learning a good data representation. Deep clustering, which utilizes deep neural networks to learn clustering-friendly representations, has gained significant attention and has been widely applied in various clustering tasks. However, existing surveys on deep clustering have primarily focused on single-view fields and network architectures, neglecting the complex application scenarios of clustering. To address this limitation, the authors of this paper present a comprehensive survey that explores deep clustering from the perspective of different data sources. The survey systematically categorizes deep clustering methods based on four distinct categories: traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. By considering different data sources and initial conditions, the authors provide insights into the methodology, prior knowledge utilization, and architectural aspects of each category. Furthermore, the paper discusses open challenges and potential future opportunities in various fields related to deep clustering. This comprehensive survey aims to bridge the gap between existing research on single-view fields and network architectures by providing a broader understanding of deep clustering in diverse application scenarios. Overall, this refined summary highlights how cluster analysis is essential in machine learning and data mining. It emphasizes the significance of learning good data representations for effective clustering algorithms. The paper's contribution lies in providing a comprehensive survey that addresses the limitations of previous research by exploring deep clustering across different data sources and application scenarios.