Compara mètodes

	Agrupació de documents	Modelatge de temes
Camp	Mineria de text	Aprenentatge profund
Família	Process / pipeline	Machine learning
Any d'origen	—	1999–2003
Autor original	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipus	Unsupervised text-mining task	Unsupervised generative probabilistic model
Font seminal	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Àlies	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Relacionats	4	5
Resum	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.