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

2026-02-05

AI Summary

This work addresses a fundamental limitation of traditional k-means clustering, which struggles to identify clusters of arbitrary shape, scale, and density. While existing deep clustering methods, such as Deep Embedded Clustering (DEC), incorporate deep representations, they generally overlook the intrinsic distributional characteristics of data clusters, thereby failing to overcome this core limitation. The paper is the first to explicitly identify this critical shortcoming and proposes a novel non-deep learning paradigm that explicitly models the distributional properties of clusters to effectively capture complex structures. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art deep clustering algorithms, successfully overcoming the inherent constraints of k-means and revealing that deep representations are not a necessary condition for addressing this challenge.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• deep clustering
• k-means limitations
• cluster shape
• data distribution
• arbitrary clusters

Innovation

• deep clustering
• k-means limitations
• distributional information
• non-deep learning
• cluster shape