Vertaile menetelmiä

	Selitettävä K-Means	Päätöspuu	Hierarkkinen ryvästyminen	K-Means-klusterointi
Tieteenala	Koneoppiminen	Koneoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning	Machine learning
Syntyvuosi	2020	1984	1963	1967
Kehittäjä	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Breiman, Friedman, Olshen & Stone	Ward, J. H.	MacQueen, J.
Tyyppi	Explainable unsupervised clustering algorithm	Recursive partitioning (if-then rules)	Unsupervised clustering (agglomerative)	Partitional clustering (centroid-based)
Alkuperäislähde	Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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.
Rinnakkaisnimet	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Liittyvät	5	5	4	3
Tiivistelmä	Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable.	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.	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.