विधियों की तुलना करें

	Latent Dirichlet Allocation (LDA)	के-मीन्स क्लस्टरिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Latent structure	Machine learning
उद्भव वर्ष	2003	1967
प्रवर्तक	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	MacQueen, J.
प्रकार	Generative probabilistic topic model (three-level hierarchical Bayesian)	Partitional clustering (centroid-based)
मौलिक स्रोत	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	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.
उपनाम	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
संबंधित	3	3
सारांश	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.	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.