Bandingkan kaedah

	Peruntukan Dirichlet Latent (LDA)	Pengelompokan K-Means	Word2Vec
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Perlombongan Teks
Keluarga	Latent structure	Machine learning	Process / pipeline
Tahun asal	2003	1967	2013
Pengasas	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	MacQueen, J.	Tomas Mikolov et al.
Jenis	Generative probabilistic topic model (three-level hierarchical Bayesian)	Partitional clustering (centroid-based)	Neural word-embedding model
Sumber perintis	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Berkaitan	3	3	4
Ringkasan	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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.