摘要
蚁群算法作为一种新型的优化方法,具有很强的适应性和鲁棒性。基于蚁群算法的聚类方法已经在当前数据挖掘研究中得到应用。文章提出了一个新颖策略来解决无人监督的数据聚类问题,利用信息素控制蚂蚁随机移动提高算法效率,采用运动速度各异的多个蚂蚁独立并行进行聚类来提高聚类质量。实验结果表明该方法是有效的。
Ant colony algorithms are robust and adaptable as novel optimization methods.The ant-based clustering algorithm has currently applications in the data mining community.This paper presents a novel strategy to tackle unsupervised data clustering problems,in order to improve efficiency of algorithms which takes pheromone to control randomly moving.Each ant takes different types of moving speeds and independently clusters to improve quality of clustering.Results show that this method is impacfful.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第20期157-159,163,共4页
Computer Engineering and Applications
关键词
蚁群算法
信息素
聚类
ant colony algonShm,phemmone,clustering
