مقایسهٔ روش‌ها

	خوشه‌بندی اسناد	TF-IDF	Thematic Analysis
حوزه	متن‌کاوی	متن‌کاوی	پژوهش کیفی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	—	1988	2006
پدیدآور	—	Salton & Buckley	Virginia Braun and Victoria Clarke
نوع	Unsupervised text-mining task	Text vectorization / term-weighting scheme	Method
منبع بنیادین	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.	Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
نام‌های دیگر	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	TA, Reflexive Thematic Analysis
مرتبط	4	3	3
خلاصه	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.	Thematic Analysis (TA) is a qualitative research methodology for identifying, analyzing, and reporting patterns (themes) in qualitative data. Developed systematically by Virginia Braun and Victoria Clarke (2006), TA is flexible and accessible, applicable across diverse theoretical frameworks and data types, making it one of the most widely used qualitative methods in psychology, health research, and social sciences.