Salīdzināt metodes

	Skaidrojamais K-Means	Hierarhiskā klasterizācija	K-Means klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2020	1963	1967
Autors	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ward, J. H.	MacQueen, J.
Tips	Explainable unsupervised clustering algorithm	Unsupervised clustering (agglomerative)	Partitional clustering (centroid-based)
Pirmavots	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.	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.
Citi nosaukumi	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Saistītās	5	4	3
Kopsavilkums	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.	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.