手法を比較

	文書クラスタリング	TF-IDF	トピックモデリング
分野	テキストマイニング	テキストマイニング	深層学習
系統	Process / pipeline	Process / pipeline	Machine learning
提唱年	—	1988	1999–2003
提唱者	—	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
種類	Unsupervised text-mining task	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
原典	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
別名	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
関連	4	3	5
概要	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).	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.	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.