Document Classification Using Itemsets

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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Authors of this publication:

Jiří Hynek

Phone: +420 603492837

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

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

Related Projects:


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.