aiqbench analysis
This research challenges the assumption that deep learning is necessary to overcome fundamental limitations of traditional clustering, such as handling arbitrary cluster shapes and densities. The authors demonstrate that leveraging distributional information through a non-deep learning approach can achieve the intended advantages previously attributed solely to deep embedded clustering methods. This finding suggests a potential shift in architectural priorities for unsupervised learning tasks where model complexity may not yield proportional performance gains.
How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
arXiv:2602.05749v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG)
arXiv:2602.05749 [cs.LG]
(or arXiv:2602.05749v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.05749 Focus to learn more arXiv-issued DOI via DataCite
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