Apriori and GUHA – Comparing two approaches to data mining with association rules

Article type: Research Article

Authors: Rauch, Jan^* | Šimůnek, Milan

Affiliations: Department of Information and Knowledge Engineering, University of Economics, Prague, Czech Republic

Correspondence: [*] Corresponding author: Jan Rauch, Department of Information and Knowledge Engineering, University of Economics, nám. W. Churchilla 4, 13067, Prague, Czech Republic. E-mail: rauch@vse.cz.

Abstract: Two approaches to data mining with association rules are compared – the apriori algorithm and the ASSOC procedure. The first one was developed for market basket analysis at the beginning of 1990s. An association rule is understood as an implication between conjunctions of attribute-value pairs. The ASSOC procedure is an implementation of the GUHA method of mechanizing hypothesis formation developed since the 1960s. ASSOC deals with association rules – general relations of two general Boolean attributes. Arules – a computational environment for mining association rules based on apriori and the 4ft-Miner procedure – an implementation of the ASSOC procedure are discussed and compared. It is shown that the arules approach to missing information does not correspond to Kleene’s approach and this can lead to a large number of misleading rules. It is also shown that a secured completion developed for the ASSOC procedure avoids this problem.

Keywords: Association rules, apriori, GUHA, arules, 4ft-Miner

DOI: 10.3233/IDA-160069

Journal: Intelligent Data Analysis, vol. 21, no. 4, pp. 981-1013, 2017