Sammenlign metoder

	Affinity Propagation Clustering	Spektral klyngedannelse
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2007	2002
Ophavsperson	Brendan Frey & Delbert Dueck	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Exemplar-based clustering via message passing	Graph-based clustering (spectral method)
Oprindelig kilde	Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Aliasser	affinity propagation clustering, message-passing clustering, exemplar-based clustering, yakınlık yayılımı kümeleme	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Relaterede	4	5
Resumé	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.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.