A framework for deep constrained clustering

• Published in: Data mining and knowledge discovery Vol. 35; no. 2; pp. 593 - 620
• Main Authors: Zhang, Hongjing, Zhan, Tianyang, Basu, Sugato, Davidson, Ian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Clustering; Computer Science; Constraints; Data Mining and Knowledge Discovery; Deep learning; Formulations; Information Storage and Retrieval; Machine learning; Physics; Source code; Statistics for Engineering; Deep learning; Representation learning; Semi-supervised learning; Constrained clustering
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-020-00734-4

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. Furthermore, we propose an efficient training paradigm that is generally applicable to these four types of constraints. We validate the effectiveness of our approach by empirical results on both image and text datasets. We also study the robustness of our framework when learning with noisy constraints and show how different components of our framework contribute to the final performance. Our source code is available at: http://github.com/blueocean92 .