পদ্ধতির তুলনা করুন

	K-Means ক্লাস্টারিং	Hierarchical Clustering	রৈখিক বৈষম্যমূলক বিশ্লেষণ (LDA
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Machine learning	Machine learning	Hypothesis test
উদ্ভবের বছর	1967	1963	1936
প্রবর্তক	MacQueen, J.	Ward, J. H.	Ronald A. Fisher
ধরন	Partitional clustering (centroid-based)	Unsupervised clustering (agglomerative)	Parametric linear classifier / dimensionality reduction
মৌলিক উৎস	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.
অপর নাম	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis
সম্পর্কিত	3	4	7
সারসংক্ষেপ	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.	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.	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.