Bandingkan kaedah

	Pemodelan Topik NMF	Pengelompokan Dokumen
Bidang	Perlombongan Teks	Perlombongan Teks
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1999	—
Pengasas	Lee & Seung	—
Jenis	Matrix-factorization topic model	Unsupervised text-mining task
Sumber perintis	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227
Alias	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)
Berkaitan	4	4
Ringkasan	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	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).