Linganisha mbinu

	Upangaji wa Uenezaji wa Ukaribu	DBSCAN	K-Means Clustering
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2007	1996	1967
Mwanzilishi	Brendan Frey & Delbert Dueck	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J.
Aina	Exemplar-based clustering via message passing	Density-based clustering algorithm	Partitional clustering (centroid-based)
Chanzo asilia	Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	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.
Majina mbadala	affinity propagation clustering, message-passing clustering, exemplar-based clustering, yakınlık yayılımı kümeleme	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Zinazohusiana	4	3	3
Muhtasari	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.