Methoden vergelijken

	Word2Vec	Documentclustering	TF-IDF
Vakgebied	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Jaar van ontstaan	2013	—	1988
Grondlegger	Tomas Mikolov et al.	—	Salton & Buckley
Type	Neural word-embedding model	Unsupervised text-mining task	Text vectorization / term-weighting scheme
Oorspronkelijke bron	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.	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.
Aliassen	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Verwant	4	4	3
Samenvatting	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.	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.