TY - CONF

T1 - A Constrained Cluster Ensemble Using Hierarchical Clustering Methods

AU - Williams, Frank

AU - Kuncheva, Ludmila

AU - Hennessey, Sam

AU - Diez-Pastor, Jose

AU - Rodriguez, Juan

PY - 2024/8

Y1 - 2024/8

N2 - Unsupervised classification of data is an ongoing challenge in many areas. With evolving stream data, hierar-chical clustering methods have proved effective, especially with non-spherical clusters. Additionally, incorporating pairwise con-straints has been shown to further improve clustering accuracy. We propose a cluster ensemble using constrained hierarchical methods. The experiment was performed on a collection of 52 Synthetic and 96 Real datasets. Our analysis shows that our constrained cluster ensemble method results in a high accuracy across various proportions of constraints without sacrificing speed

AB - Unsupervised classification of data is an ongoing challenge in many areas. With evolving stream data, hierar-chical clustering methods have proved effective, especially with non-spherical clusters. Additionally, incorporating pairwise con-straints has been shown to further improve clustering accuracy. We propose a cluster ensemble using constrained hierarchical methods. The experiment was performed on a collection of 52 Synthetic and 96 Real datasets. Our analysis shows that our constrained cluster ensemble method results in a high accuracy across various proportions of constraints without sacrificing speed

U2 - 10.1109/IS61756.2024.10705267

DO - 10.1109/IS61756.2024.10705267

M3 - Paper

T2 - 2024 IEEE 12th International Conference on Intelligent Systems (IS)

Y2 - 29 August 2024 through 31 August 2024

ER -