পদ্ধতির তুলনা করুন

	Explainable K-Means	ডিবিস্ক্যান	সিদ্ধান্ত বৃক্ষ	K-Means ক্লাস্টারিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2020	1996	1984	1967
প্রবর্তক	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Breiman, Friedman, Olshen & Stone	MacQueen, J.
ধরন	Explainable unsupervised clustering algorithm	Density-based clustering algorithm	Recursive partitioning (if-then rules)	Partitional clustering (centroid-based)
মৌলিক উৎস	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	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.
অপর নাম	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
সম্পর্কিত	5	3	5	3
সারসংক্ষেপ	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.