A Framework for Deep Constrained Clustering

TL;DR

This paper introduces a flexible deep learning framework for constrained clustering that handles various types of constraints, including complex and high-level domain knowledge, demonstrating effectiveness on image and text datasets.

Contribution

It presents a novel deep learning-based framework that extends constrained clustering to complex constraints and offers an efficient training paradigm validated on multiple datasets.

Findings

Effective handling of standard and complex constraints

Robustness to noisy constraints demonstrated

Improved clustering performance on image and text data

Abstract

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 $\href{https://github.com/blueocean92/deep_constrained_clustering}{\text{URL}}$.