Vertaile menetelmiä

	Päätöspuu	K-Means-klusterointi
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1984	1967
Kehittäjä	Breiman, Friedman, Olshen & Stone	MacQueen, J.
Tyyppi	Recursive partitioning (if-then rules)	Partitional clustering (centroid-based)
Alkuperäislähde	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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.
Rinnakkaisnimet	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Liittyvät	5	3
Tiivistelmä	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.