مقایسهٔ روش‌ها

	خوشه‌بندی انتشار وابستگی	خوشه‌بندی سلسله‌مراتبی	خوشه‌بندی K-Means
حوزه	یادگیری ماشین	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning
سال پیدایش	2007	1963	1967
پدیدآور	Brendan Frey & Delbert Dueck	Ward, J. H.	MacQueen, J.
نوع	Exemplar-based clustering via message passing	Unsupervised clustering (agglomerative)	Partitional clustering (centroid-based)
منبع بنیادین	Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	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.
نام‌های دیگر	affinity propagation clustering, message-passing clustering, exemplar-based clustering, yakınlık yayılımı kümeleme	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
مرتبط	4	4	3
خلاصه	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	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.