Vertaile menetelmiä

	K-Means-klusterointi	Paikallisesti lineaarinen upotus (LLE)
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1967	2000
Kehittäjä	MacQueen, J.	Sam Roweis & Lawrence Saul
Tyyppi	Partitional clustering (centroid-based)	Nonlinear manifold dimensionality reduction
Alkuperäislähde	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.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
Rinnakkaisnimet	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
Liittyvät	3	3
Tiivistelmä	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.	Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.