Deep Clustering: A Comprehensive Survey

TL;DR

This survey comprehensively reviews deep clustering methods across various data sources, categorizing approaches by methodology, prior knowledge, and architecture, and discusses future challenges and opportunities.

Contribution

It provides a systematic classification of deep clustering methods based on data sources and initial conditions, expanding beyond existing surveys focused on single-view and architecture details.

Findings

Deep clustering methods are categorized into four main types.

The survey highlights the importance of data source diversity in clustering.

Open challenges and future directions are identified for deep clustering.

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.