เปรียบเทียบวิธี

	การจัดกลุ่มเอกสาร	การจำแนกข้อความ	TF-IDF
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	—	—	1988
ผู้ริเริ่ม	—	—	Salton & Buckley
ประเภท	Unsupervised text-mining task	Supervised NLP classification task	Text vectorization / term-weighting scheme
แหล่งต้นตำรับ	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
ชื่อเรียกอื่น	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
ที่เกี่ยวข้อง	4	4	3
สรุป	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).	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.	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.