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Semi-supervised clustering ensemble based on genetic algorithm model
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AI
DOI：10.1007/s11042-023-17662-2.png)
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Abstract
En 中文
Clustering ensemble can be regarded as a mathematical optimization problem, and the genetic algorithm has been widely used as a powerful tool for solving such optimization problems. However, the existing research on clustering ensemble based on the genetic algorithm model has mainly focused on unsupervised approaches and has been limited by parameters like crossover probability and mutation probability. This paper presents a semi-supervised clustering ensemble based on the genetic algorithm model. This approach utilizes pairwise constraint information to strengthen the crossover process and mutation process, resulting in enhanced overall algorithm performance. To validate the effectiveness of the proposed approach, extensive comparative experiments were conducted on 9 diverse datasets. The results of the experiments demonstrate the superiority of the proposed algorithm in terms of clustering accuracy and robustness. In summary, this paper introduces a novel semi-supervised approach based on the genetic algorithm model. The utilization of pair-wise constraint information enhances the algorithm's performance, making it a promising solution for real-world clustering problems.
Keywords:
Nonnegative matrix factorization
Clustering ensemble
Semi-supervised learning
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W