A Constrained Cluster Ensemble Using Hierarchical Clustering Methods

Frank Williams, Ludmila Kuncheva, Sam Hennessey, Jose Diez-Pastor, Juan Rodriguez

Research output: Contribution to conferencePaperpeer-review

Abstract

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
Original languageEnglish
DOIs
Publication statusPublished - Aug 2024
Event2024 IEEE 12th International Conference on Intelligent Systems (IS) - Varna, Bulgaria
Duration: 29 Aug 202431 Aug 2024
http://10.1109/IS61756.2024.10705267

Conference

Conference2024 IEEE 12th International Conference on Intelligent Systems (IS)
Country/TerritoryBulgaria
CityVarna
Period29/08/2431/08/24
Internet address

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