Affiliation:
1. Abbes Laghrour University of Khenchela, Algeria & Batna 2 University, Algeria
2. Batna 2 University, Algeria
3. Abbes Laghrour University of Khenchela, Algeria
Abstract
Association rules are the specific data mining methods aiming to discover explicit relations between the different attributes in a large dataset. However, in reality, several datasets may contain both numeric and categorical attributes. Recently, many meta-heuristic algorithms that mimic the nature are developed for solving continuous problems. This article proposes a new algorithm, DCSA-QAR, for mining quantitative association rules based on crow search algorithm (CSA). To accomplish this, new operators are defined to increase the ability to explore the searching space and ensure the transition from the continuous to the discrete version of CSA. Moreover, a new discretization algorithm is adopted for numerical attributes taking into account dependencies probably that exist between attributes. Finally, to evaluate the performance, DCSA-QAR is compared with particle swarm optimization and mono and multi-objective evolutionary approaches for mining association rules. The results obtained over real-world datasets show the outstanding performance of DCSA-QAR in terms of quality measures.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications
Cited by
1 articles.
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1. Digital Association Rules Algorithm in Accounting Information Distortion Recognition System;2022 International Conference on Knowledge Engineering and Communication Systems (ICKES);2022-12-28