亲和繁殖
聚类分析
计算机科学
相似性(几何)
数据挖掘
集合(抽象数据类型)
数据集
数据点
星团(航天器)
模式识别(心理学)
人工智能
模糊聚类
CURE数据聚类算法
图像(数学)
程序设计语言
作者
Brendan J. Frey,Delbert Dueck
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2007-02-16
卷期号:315 (5814): 972-976
被引量:5724
标识
DOI:10.1126/science.1136800
摘要
Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.
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