방법 비교

	문서 군집화	키워드 추출	토픽 모델링
분야	텍스트 마이닝	텍스트 마이닝	딥러닝
계열	Process / pipeline	Process / pipeline	Machine learning
기원 연도	—	—	1999–2003
창시자	—	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
유형	Unsupervised text-mining task	NLP text-mining task	Unsupervised generative probabilistic model
원전	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
별칭	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
관련	4	4	5
요약	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).	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	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.