A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions (2206.07579v1)

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.

• The paper introduces a comprehensive taxonomy for deep clustering, categorizing methods into multi-stage, iterative, generative, and simultaneous approaches.
• The paper examines challenges including effective initialization, scalability, and handling overlapping or anomalous data in clustering.
• The survey outlines future directions such as leveraging transfer learning and robust integration of representation learning to enhance clustering performance.

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

The reviewed paper presents an extensive survey on deep clustering, a field that has increasingly gained traction owing to the limitations of shallow clustering methods in handling high-dimensional data. This research aims to categorize, analyze, and suggest future pathways for methodologies that integrate deep learning into clustering tasks.

Overview of Deep Clustering

Deep clustering distinguishes itself by leveraging deep neural networks to jointly optimize both representation learning and clustering processes. This integration is crucial as it directly addresses challenges where instance relationships and data complexity surpass the capabilities of traditional clustering techniques. The paper categorizes existing methodologies and explores the symbiosis between clustering performance and representation learning using deep architectures.

Taxonomy and Methodological Insights

The survey introduces a novel taxonomy that organizes the spectrum of deep clustering methods into four primary categories based on their operational design:

1. Multi-Stage Deep Clustering: Methods in this category perform sequential operations where deep learning is utilized primarily for representation learning before conventional clustering is applied. This structure retains simplicity but may result in suboptimal performance due to limited interaction between stages.
2. Iterative Deep Clustering: These approaches emphasize an iterative refinement process where clustering results and representations are alternately improved. The interplay aims to rectify early-phase errors and refine data representations for enhanced clustering.
3. Generative Deep Clustering: This class utilizes deep generative models like VAEs and GANs to model latent cluster structures, offering the benefit of modeling complex data distributions. However, challenges such as convergence and computational overhead remain.
4. Simultaneous Deep Clustering: These methods conduct representation learning and clustering in a unified framework, allowing mutual reinforcement. Such integration can lead to more robust clustering results but requires careful balancing to avoid degenerate solutions, where output clusters are trivial.

Challenges and Future Directions

The paper addresses several pressing challenges within deep clustering, recommending areas for future exploration:

• Initialization and Scalability: Effective initialization strategies and scalable methods remain critical, especially concerning large-scale datasets with intricate structures.
• Handling Overlapping and Anomalous Data: Current methods are primarily focused on partitioning tasks; thus, approaches that handle overlapping clusters and anomalies effectively are needed.
• Transfer Learning and Robustness: Emphasizing transfer learning for knowledge generalization and robustness against unbalanced or outlier-containing data sets is highlighted as a crucial future direction.

Practical Implications and Research Pathways

The implications of deep clustering span various domains, including community detection, anomaly detection, and more. For instance, anomaly detection benefits from the clustering of data points to highlight deviations within clusters, enhancing identification accuracy. Moreover, the survey invites exploration into the auxiliary applications of clustering within broader AI frameworks, contributing to a more integrated understanding of data utilization.

Conclusion

This survey underscores the evolution of clustering methodologies through the integration of deep learning techniques. By providing a comprehensive taxonomy and discussing challenges and opportunities, it establishes a foundation for future research and application, stimulating continued development in the field of deep clustering. The integration of representation learning with clustering tasks highlights the potential of deep clustering to address complex, high-dimensional data more effectively than traditional methods.