Combining Multiple Clusterings using Information Theory based Genetic Algorithm
Huilan Luo, Furong Jing, Xiaobing Xie · 2006
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple clusterings is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the genetic algorithm based on information theory is proposed in this paper. A combined clustering is found by minimizing an information-theoretical criterion function using genetic algorithm. This study compares the performance of the information-theoretical consensus algorithm with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method