文章基本信息

标题：An Improved Clustering Algorithm Based on Density Distribution Function
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作者：Jianhao Tan ; Jing Zhang ; Weixiong Li 等
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2010
卷号：3
期号：3
页码：23
DOI：10.5539/cis.v3n3P23
出版社：Canadian Center of Science and Education
摘要：Some characteristics and week points of traditional density-based clustering algorithms are deeply analysed , then an improved way based on density d istribution function i s put forward. K Nearest Neighbor( KNN ) is used to measure the density of each point, then 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 find whether it is a core point by a radius scale factor. Then 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. The tests show that the improved algorithm g reatly improves the sensitivity of density-based clustering algorithms to parameters and enhances the clustering effect of the high-dimensional data sets with uneven density d istribution.