【摘要】 聚类分析一直是机器学习和数据挖掘领域一个比较活跃而且极具挑战性的研究方向。Alex提出的基于局部密度的聚类算法是一种快速、有效的聚类方法,但该方法通过手工选取确定聚类个数和聚类中心。为此,对原算法进行改进,在初步选取候选聚类中心的基础上,使用基于密度连通的算法优化选取聚类中心,然后使用大密度最近邻方法确定样本类别。实验证明,该方法能有效解决聚类个数和聚类中心无法确定的问题,同时在聚类评价指标上显示出较好的聚类效果和性能。更多还原

【Abstract】 Clustering analysis is an important and challenging research field in machine learning and data mining.A fast and effective clustering algorithm based on the idea of local density was proposed by Alex.But the number of clusters and cluster centers in the algorithm were determined by hand.Therefore,the candidates of cluster centers based on local density were firstly selected and then density connectivity method was used to optimize the candidates.The classes of samples are the same as the nearest center with bigger local density.Experiments show that the proposed method has a better cluster efficiency and can handle the problems of uncertain cluster number and cluster centers.更多还原