Vertaile menetelmiä

	Dokumenttien klusterointi	Word2Vec
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	—	2013
Kehittäjä	—	Tomas Mikolov et al.
Tyyppi	Unsupervised text-mining task	Neural word-embedding model
Alkuperäislähde	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Rinnakkaisnimet	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Liittyvät	4	4
Tiivistelmä	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).	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.