Szczegóły publikacji
Opis bibliograficzny
Text mining with hybrid biclustering algorithms / Patryk ORZECHOWSKI, Krzysztof BORYCZKO // W: Artificial intelligence and soft computing : 15th international conference, ICAISC 2016 : Zakopane, Poland, June 12–16, 2016 : proceedings, Pt. 2 / eds. Leszek Rutkowski, [et al.]. — Switzerland : Springer International Publishing, cop. 2016. — (Lecture Notes in Artificial Intelligence ; ISSN 0302-9743 ; 9693). — ISBN: 978-3-319-39383-4; e-ISBN: 978-3-319-39384-1. — S. 102–113. — Bibliogr. s. 111–113, Abstr. — Toż na Dysku Flash
Autorzy (2)
Dane bibliometryczne
ID BaDAP | 98335 |
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Data dodania do BaDAP | 2016-06-21 |
DOI | 10.1007/978-3-319-39384-1_9 |
Rok publikacji | 2016 |
Typ publikacji | materiały konferencyjne (aut.) |
Otwarty dostęp | |
Konferencja | 15th International Conference on Artificial Intelligence and Soft Computing |
Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Text data mining is the process of extracting valuable information from a dataset consisting of text documents. Popular clustering algorithms do not allow detection of the same words appearing in multiple documents. Instead, they discover general similarity of such documents. This article presents the application of a hybrid biclustering algorithm for text mining documents collected from Twitter and symbolic analysis of knowledge spreadsheets. The proposed method automatically reveals words appearing together in multiple texts. The proposed approach is compared to some of the most recognized clustering algorithms and shows the advantage of biclustering over clustering in text mining. Finally, the method is confronted with other biclustering methods in the task of classification. © Springer International Publishing Switzerland 2016.