A novel concurrent relational association rule mining approach

Data mining techniques are intensively used to uncover relevant patterns in large volumes of complex data which are continuously extended with newly arrived data instances. Relational association rules (RARs), a data analysis and mining concept, have been introduced as an extension of classical association rules (ARs) for capturing various relationships between the attributes characterizing the data. Due to its NP-completeness, the problem of mining all the interesting RARs within a data set is computationally difficult. As the dimensionality of the data set to be mined increases, the classical algorithm Discovery of Relational Association Rules (DRAR) for RARs mining fails in providing the set of rules in reasonable time. This paper introduces a new approach named CRAR (Concurrent Relational Association Rule mining) which uses concurrency for the RARs discovery process and thus significantly reduces the mining time. The effectiveness of CRAR is empirically validated on nine open source data sets. The reduction in mining time when using CRAR against DRAR emphasizes that it can be successfully applied in various practical data mining scenarios. (C) 2019 Elsevier Ltd. All rights reserved.