EK-Means: Towards Making Ensemble K-Means Work for Image-Based Data Analysis Without Prior Knowledge of K

Danping Niu, Danping Niu, Danping Niu, Yuan Ping, Yuan Ping, Yujian Liu, Yujian Liu, Fanxi Wei, Fanxi Wei, Wenhong Wu

2025

Abstract

Despite its widespread application, K-means is significantly constrained by its dependence on the prior knowledge and its limitations in handling irregular data patterns, which restrict its performance in practical scenarios such as malware detection. To address these shortcomings, a novel EK-means algorithm is proposed. It introduces a dynamic cluster adaptation strategy (DCAS) to leverage similarity and separation measures in the pre-clustering phase to enable adaptive splitting and merging of clusters. The continuous refinement of cluster compactness and centroid representativeness in this approach facilitates the discovery of clusters with arbitrary shapes and the automatic discovery of the true number of clusters. Experimental results show that EK-means achieves high clustering accuracy across multiple datasets, including Fashion-MNIST, Virus MNIST, BIG 2015, and Malimg. It notably excels in malware detection tasks, outperforming some existing mainstream K-means enhancement methods.

Paper Citation

in Harvard Style

Niu D., Ping Y., Liu Y., Wei F. and Wu W. (2025). EK-Means: Towards Making Ensemble K-Means Work for Image-Based Data Analysis Without Prior Knowledge of K. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 575-584. DOI: 10.5220/0013250100003899

in Bibtex Style

@conference{icissp25,
author={Danping Niu and Yuan Ping and Yujian Liu and Fanxi Wei and Wenhong Wu},
title={EK-Means: Towards Making Ensemble K-Means Work for Image-Based Data Analysis Without Prior Knowledge of K},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={575-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013250100003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - EK-Means: Towards Making Ensemble K-Means Work for Image-Based Data Analysis Without Prior Knowledge of K
SN - 978-989-758-735-1
AU - Niu D.
AU - Ping Y.
AU - Liu Y.
AU - Wei F.
AU - Wu W.
PY - 2025
SP - 575
EP - 584
DO - 10.5220/0013250100003899
PB - SciTePress