A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

• URL: http://arxiv.org/abs/2206.07579v1
• Date: Wed, 15 Jun 2022 15:05:13 GMT
• Title: A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
• Authors: Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
• Abstract summary: Clustering is a fundamental machine learning task which has been widely studied in the literature. Deep Clustering, i.e., jointly optimizing the representation learning and clustering, has been proposed and hence attracted growing attention in the community. We summarize the essential components of deep clustering and categorize existing methods by the ways they design interactions between deep representation learning and clustering.
• Score: 48.97008907275482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation learning techniques. As the data become increasingly complicated and complex, the shallow (traditional) clustering methods can no longer handle the high-dimensional data type. With the huge success of deep learning, especially the deep unsupervised learning, many representation learning techniques with deep architectures have been proposed in the past decade. Recently, the concept of Deep Clustering, i.e., jointly optimizing the representation learning and clustering, has been proposed and hence attracted growing attention in the community. Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches. We summarize the essential components of deep clustering and categorize existing methods by the ways they design interactions between deep representation learning and clustering. Moreover, this survey also provides the popular benchmark datasets, evaluation metrics and open-source implementations to clearly illustrate various experimental settings. Last but not least, we discuss the practical applications of deep clustering and suggest challenging topics deserving further investigations as future directions.

Related papers

• A Survey on Deep Clustering: From the Prior Perspective [9.628838389364864]
  This survey provides a comprehensive review of deep clustering methods by categorizing them into six types of prior knowledge. We provide a benchmark on five widely-used datasets and analyze the performance of methods with diverse priors.
  arXiv  Detail & Related papers  (2024-06-28T02:18:16Z)
• Snapshot Spectral Clustering -- a costless approach to deep clustering ensembles generation [0.0]
  This paper proposes a novel deep clustering ensemble method - Snapshot Spectral Clustering. It is designed to maximize the gain from combining multiple data views while minimizing the computational costs of creating the ensemble.
  arXiv  Detail & Related papers  (2023-07-17T16:01:22Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Deep Clustering: A Comprehensive Survey [53.387957674512585]
  Clustering analysis plays an indispensable role in machine learning and data mining. 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.
  arXiv  Detail & Related papers  (2022-10-09T02:31:32Z)
• DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks [53.88811980967342]
  This paper presents a Deep Clustering via Ensembles (DeepCluE) approach. It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
  arXiv  Detail & Related papers  (2022-06-01T09:51:38Z)
• A Survey of Evolutionary Multi-Objective Clustering Approaches [0.0]
  We analyze the algorithms based on the features and components presented in the proposed general architecture of the evolutionary multi-objective clustering. It is essential to observe these aspects besides specific clustering properties when designing new approaches or selecting/using the existing ones.
  arXiv  Detail & Related papers  (2021-10-15T13:53:12Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.