控制理论与应用2011，Vol.28Issue(12)：1791-1796,6.
密度分布函数在聚类算法中的应应用用
Application of density distribution function in clustering algorithms
摘要
Abstract
Characteristics and disadvantages of traditional density-based clustering algorithms are deeply investigated; the present research status of density-based clustering algorithms is discussed; an improved clustering algorithm based on density distribution function is put forward. K nearest neighbor （KNN） is used to measure the density of each point; a local maximum density point is defined as the center point. By means of local scale, classification is extended from the center point. For each point there is a procedure to determine whether it is a core point by a radius scale factor. The classification is extended once again from the core point until the density descends to the given ratio of the density of the center point. Several algorithm examples are given and the algorithm is experimentally compared with the grid-shared nearest neighbor （GNN） clustering algorithm, on the clustering accuracy ratio and efficiency. The tests show that the improved algorithm greatly reduces the sensitivity of density-based clustering algorithms to parameters, improves the clustering effect of the high-dimensional data sets with uneven density distribution, and enhances the clustering accuracy and efficiency.关键词
聚类算法/KNN/GNN/密度分布函数/OPTICS/DENCLUE/区域比例/半径比例因子Key words
clustering algorithms/KNN/GNN/density distribution function/OPTICS（ordering points to identify the clustering structure）/DENCLUE（density-based clustering）/local scale/radius scale factor分类
信息技术与安全科学引用本文复制引用
谭建豪,章兢,李伟雄..密度分布函数在聚类算法中的应应用用[J].控制理论与应用,2011,28(12):1791-1796,6.基金项目
国家自然科学基金资助项目 （）
湖南省自然科学基金资助项目 （）
中央高校基本科研业务费资助项目 （）
