How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

• URL: http://arxiv.org/abs/2602.05749v1
• Date: Thu, 05 Feb 2026 15:16:04 GMT
• Title: How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
• Authors: Kai Ming Ting, Wei-Jie Xu, Hang Zhang,
• Abstract summary: Deep Embedded Clustering learns a latent representation via an autoencoder and performs clustering based on a $k$-means-like procedure.<n>This paper investigates whether the deep-learned representation has enabled DEC to overcome the known fundamental limitations of $k$-means clustering.
• Score: 9.022973688786545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep clustering (DC) is often quoted to have a key advantage over $k$-means clustering. Yet, this advantage is often demonstrated using image datasets only, and it is unclear whether it addresses the fundamental limitations of $k$-means clustering. Deep Embedded Clustering (DEC) learns a latent representation via an autoencoder and performs clustering based on a $k$-means-like procedure, while the optimization is conducted in an end-to-end manner. This paper investigates whether the deep-learned representation has enabled DEC to overcome the known fundamental limitations of $k$-means clustering, i.e., its inability to discover clusters of arbitrary shapes, varied sizes and densities. Our investigations on DEC have a wider implication on deep clustering methods in general. Notably, none of these methods exploit the underlying data distribution. We uncover that a non-deep learning approach achieves the intended aim of deep clustering by making use of distributional information of clusters in a dataset to effectively address these fundamental limitations.

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