聚类分析
马克西玛
度量(数据仓库)
最大值和最小值
系列(地层学)
数学
统计物理学
计算机科学
数据点
星团(航天器)
统计
数据挖掘
算法
物理
生物
数学分析
艺术
艺术史
古生物学
程序设计语言
表演艺术
作者
Álex Rodríguez,Alessandro Laio
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2014-06-27
卷期号:344 (6191): 1492-1496
被引量:3525
标识
DOI:10.1126/science.1242072
摘要
Discerning clusters of data points Cluster analysis is used in many disciplines to group objects according to a defined measure of distance. Numerous algorithms exist, some based on the analysis of the local density of data points, and others on predefined probability distributions. Rodriguez and Laio devised a method in which the cluster centers are recognized as local density maxima that are far away from any points of higher density. The algorithm depends only on the relative densities rather than their absolute values. The authors tested the method on a series of data sets, and its performance compared favorably to that of established techniques. Science , this issue p. 1492
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