Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Le regroupement de documents× | Modélisation par sujets× | |
|---|---|---|
| Domaine≠ | Fouille de textes | Apprentissage profond |
| Famille≠ | Process / pipeline | Machine learning |
| Année d'origine≠ | — | 1999–2003 |
| Auteur d'origine≠ | — | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Type≠ | Unsupervised text-mining task | Unsupervised generative probabilistic model |
| Source fondatrice≠ | Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227 | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Alias≠ | text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering) | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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). | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
| ScholarGateJeu de données ↗ |
|
|