摘要
基于密度的聚类算法因其抗噪声能力强和能发现任意形状的簇等优点,在聚类分析中被广泛采用。提出的基于相对密度的聚类算法,在继承上述优点的基础上,有效地解决了基于密度的聚类结果对参数值过于敏感、参数值难以设置以及高密度簇完全被相连的低密度簇所包含等问题。
With strong ability of discovery of arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining. A clustering algorithm based on relative density is provided, which efficiently resolves these problems of being very sensitive to the useR- defined parameters and too difficult for users to determine the parameters.
出处
《科学技术与工程》
2006年第15期2272-2276,共5页
Science Technology and Engineering
基金
国家自然科学基金(60172012)资助
关键词
聚类K近邻
聚类参数
相对密度
clustering
k-nearest neighbors
clustering parameter
relative density
