方法对比

	亲和传播聚类	K-Means聚类
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2007	1967
提出者	Brendan Frey & Delbert Dueck	MacQueen, J.
类型	Exemplar-based clustering via message passing	Partitional clustering (centroid-based)
开创性文献	Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
别名	affinity propagation clustering, message-passing clustering, exemplar-based clustering, yakınlık yayılımı kümeleme	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
相关	4	3
摘要	Affinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messages between every pair of points until a consistent set of clusters emerges. Unlike k-means it does not require the number of clusters to be specified in advance — that number arises from the data and a 'preference' parameter — and it works directly from pairwise similarities, which need not be a metric.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.