Salīdzināt metodes

	Fuzzy C-Means Clustering (FCM)	K-Means klasterizācija	Spektrālā klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	1981	1967	2002
Autors	Joseph Dunn; James Bezdek	MacQueen, J.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tips	Soft (fuzzy) partitional clustering	Partitional clustering (centroid-based)	Graph-based clustering (spectral method)
Pirmavots	Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.	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.	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.
Citi nosaukumi	FCM, fuzzy clustering, soft k-means, bulanık c-ortalama kümeleme	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Saistītās	3	3	5
Kopsavilkums	Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.	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.	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.