摘要
提出了一种以迭代增强和空间划分为基础的模糊C均值聚类方法,利用弱学习理论在每次迭代之后将产生的训练集合重新归并,在原有划分集的基础上通过分布质量权重选举方法更新产生最优假设划分集,克服了传统的简单重复训练方法的聚类效果不理想的缺点。通过形状分类实验和聚类量化指标对比,证明了该方法具有分类质量高、形状分割彻底的优点。
A hybrid Iterate boosting and space portioning clustering based FCM algorithm is proposed.Under the foundation of original portion set,the iterated optimize cluster hypothesis updates with the fraction of distribution weight voting,making use of the weak learning exoteric to remerge with the training set after every iterate process.Compared with the traditional simple repeat training clustering method,a disadvantage has been got over.It confirms that the algorithm has high quality of classification and entirely shape portioning effect in the shape classification experiment and quantity index contrast.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第34期158-160,共3页
Computer Engineering and Applications
基金
广西科学研究与技术开发计划项目(No.桂科攻10100002-2号)
关键词
迭代增强
聚类
空间划分
弱学习
iterate boosting
clustering
space portioning
weak learning
