Clustering by fast search and find of density peaks

被引:3839
作者
Rodriguez, Alex [1 ]
Laio, Alessandro [1 ]
机构
[1] SISSA, I-34136 Trieste, Italy
关键词
D O I
10.1126/science.1242072
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
a Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded fromthe analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space inwhich they are embedded. We demonstrate the power of the algorithm on several test cases.
引用
收藏
页码:1492 / 1496
页数:5
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