방법 비교

	잠재 디리클레 할당 (Latent Dirichlet Allocation, LDA)	K-평균 군집화	음이 아닌 행렬 분해(NMF)
분야	머신러닝	머신러닝	머신러닝
계열	Latent structure	Machine learning	Latent structure
기원 연도	2003	1967	1999
창시자	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	MacQueen, J.	Lee, D. D. & Seung, H. S.
유형	Generative probabilistic topic model (three-level hierarchical Bayesian)	Partitional clustering (centroid-based)	Matrix decomposition with non-negativity constraints
원전	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.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
관련	3	3	4
요약	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.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.