A Constrained Cluster Ensemble Using Hierarchical Clustering Methods
Research output: Contribution to conference › Paper › peer-review
Electronic versions
DOI
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 language | English |
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| DOIs | |
| Publication status | Published - Aug 2024 |
| Event | 2024 IEEE 12th International Conference on Intelligent Systems (IS) - Varna, Bulgaria Duration: 29 Aug 2024 → 31 Aug 2024 http://10.1109/IS61756.2024.10705267 |
Conference
| Conference | 2024 IEEE 12th International Conference on Intelligent Systems (IS) |
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| Country/Territory | Bulgaria |
| City | Varna |
| Period | 29/08/24 → 31/08/24 |
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