Comparar métodos

	Clasificación de Texto	Agrupación de documentos
Campo	Minería de texto	Minería de texto
Familia	Process / pipeline	Process / pipeline
Año de origen	—	—
Autor original	—	—
Tipo	Supervised NLP classification task	Unsupervised text-mining task
Fuente seminal	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
Alias	text categorization, document classification, topic classification, metin sınıflandırma	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)
Relacionados	4	4
Resumen	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).