Text-Mining Research Group

Document Classification Using Itemsets

The essential point of this paper is to develop a method for automating time-consuming document classification in a digital library. The method proposed in this paper is based on itemsets, extending traditional application of the apriori algorithm.

Keywords: itemset, classification, class generation, cluster, clustering, apriori algorithm, document similarity, document categorization

Year: 2000

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Jiří Hynek

Phone: +420 603492837
E-mail: jhynek@kiv.zcu.cz
WWW: http://www.kiv.zcu.cz/staff/osobni.php?id_osoby=147&lang=EN

Jiri, a co-founder of the Text-Mining Research Group, works as a lecturer at the Dept. of Computer Science and Engineering. His research interests include machine learning and language-related problems. Jiri’s teaching activity is focused on good writing style and technical writing in general.

Karel Ježek

Phone:  +420 377632475
E-mail: jezek_ka@kiv.zcu.cz
WWW: https://cs.wikipedia.org/wiki/Karel_Je%C5%BEek_(informatik)

Karel is the former group coordinator and a supervisor of PhD students working at research projects of this Group.

Document Classification
Authors:	Jiří Hynek, Karel Ježek, Michal Toman, Roman Tesař, Zdeněk Češka, Petr Grolmus
Desc.:	Use of inductive machine learning methods in classification of short text documents.