Usporedite metode

	Grupisanje dokumenata	GloVe ugrađivanja	TF-IDF
Područje	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	—	2014	1988
Tvorac	—	Pennington, Socher & Manning	Salton & Buckley
Vrsta	Unsupervised text-mining task	Static word-embedding model	Text vectorization / term-weighting scheme
Temeljni izvor	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Drugi nazivi	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Srodne	4	3	3
Sažetak	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).	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.	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.