摘要
聚类问题在一定条件下可以归结为一个带约束的优化问题。遗传算法作为一种鲁棒性很强的优化算法,具有很强的全局寻优能力。提出了一种基于C-均值和带免疫机制的混合遗传算法。理论分析和仿真实验表明,该算法既具有很强的全局寻优能力,也具有较强的局部寻优能力。
Cluster analysis is a kind of unsupervised learning method, which can extract the hidden rules from the feature data set of the objects. Clustering can be regarded as a constrained optimization problem under certain conditions. As a robust optimizing method, genetic algorithm has shown great global searching capability, which is independent of the problem domain. This paper proposes an improved hybrid genetic algorithm based on C-means and immune principle. Theoretical analysis and experiments show that this method outperforms the existing genetic clustering algorithms in both global and local convergence speed.
出处
《计算机工程》
CAS
CSCD
北大核心
2003年第12期65-66,194,共3页
Computer Engineering
基金
国家自然科学基金资助项目 (69875014)