विधियों की तुलना करें

	पाठ वर्गीकरण	दस्तावेज़ क्लस्टरिंग	TF-IDF
क्षेत्र	पाठ खनन	पाठ खनन	पाठ खनन
परिवार	Process / pipeline	Process / pipeline	Process / pipeline
उद्भव वर्ष	—	—	1988
प्रवर्तक	—	—	Salton & Buckley
प्रकार	Supervised NLP classification task	Unsupervised text-mining task	Text vectorization / term-weighting scheme
मौलिक स्रोत	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	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.
उपनाम	text categorization, document classification, topic classification, metin sınıflandırma	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
संबंधित	4	4	3
सारांश	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.