Comprehensive concept description based on association rules: A meta-learning approach

Article type: Research Article

Authors: Berka, Petr

Affiliations: Department of Information and Knowledge Engineering, University of Economics, W. Churchill Sq. 4, 130 67 Prague, Czech Republic | E-mail: [email protected]

Abstract: This paper presents a novel approach to post-processing of association rules based on the idea of meta-learning. A subsequent association rule mining step is applied to the results of “standard” association rule mining. We thus obtain “rules about rules”, which can help us better understand the association rules generated in the first step. We define various types of such meta-rules and report some experiments on benchmark data from the UCI Machine Learning Repository as well as on data from atherosclerosis risk domain. When evaluating the proposed method, we use the LISp-Miner system.

Keywords: Association rules, meta-learning, LISp-Miner

DOI: 10.3233/IDA-163307

Journal: Intelligent Data Analysis, vol. 22, no. 2, pp. 325-344, 2018