An Optimization Driven Consensus Framework for Ensemble Clustering Using Particle Swarm Optimization
Keywords:
Ensemble clustering, Consensus clustering, Particle swarm optimization, Label space optimization, Unsupervised learning, Clustering error rateAbstract
Clustering ensemble methods aim to improve robustness by integrating multiple base partitions; however, many existing consensus strategies rely on heuristic aggregation and remain sensitive to instability in individual clustering results. To address this limitation, this paper proposes Consensus Particle Swarm Clustering (CPSC), an unsupervised ensemble clustering framework that formulates consensus construction as an explicit optimization problem in the label space. In the proposed approach, multiple base clustering solutions are transformed into a unified label based representation, and Particle Swarm Optimization is employed to search for a consensus partition that maximizes agreement among ensemble members independently of the original feature space. This design enhances robustness against initialization sensitivity and variability across clustering algorithms. The effectiveness of CPSC is evaluated on standard benchmark datasets, including Iris, Diabetes, Yeast, Two Spiral, and Ralf rings. Experimental results demonstrate that CPSC consistently outperforms individual clustering methods and conventional consensus techniques in terms of on the. In particular, the proposed method achieves error rates as low as 4.00% on the Iris dataset and 31.25% on the Diabetes dataset, while yielding an average error rate of 32.32% across all evaluated benchmarks. Sensitivity analysis further indicates that CPSC exhibits stable performance with respect to the number of clusters, converging within an effective range of K. Although the framework requires the number of clusters to be specified in advance and introduces additional computational cost due to iterative optimization, the results confirm the effectiveness of optimization driven consensus formation. Overall, CPSC provides a robust and extensible solution for unsupervised ensemble clustering, offering a principled alternative to heuristic consensus methods.
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Copyright (c) 2025 Abdolrashid Rezvani, Abbas Mirzaei (Author); Nasser Mikaeilvand (Corresponding author); Babak Nouri-Moghaddam, Sajjad Jahanbakhsh Gudakahriz (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
