A Constrained Cluster Ensemble Using Hierarchical Clustering Methods
Francis J. Williams, Samuel L. Hennessey, Ludmila Ilieva Kuncheva, José-Francisco Díez-Pastor, Juan J. Rodríguez · 2024
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.