Swarm-Intelligence Algorithms for Mining Numerical Association Rules: An Exhaustive Multi-Aspect Analysis of Performance Assessment Data

37 Pages Posted: 24 Mar 2023

See all articles by Minakshi Kaushik

Minakshi Kaushik

Tallinn University of Technology (TUT)

Abstract

Numerical association rule mining (NARM) is an extended version of association rule mining that determines association rules in numerical data items, primarily via  distribution, discretization and optimization techniques. Under the umbrella of optimization techniques, several evolutionary and swarm intelligence-based algorithms have been proposed to extract association rules from a numeric dataset. However, a sufficient understanding of the performance of swarm intelligence-based algorithms, especially for NARM, is still missing. In state-of-the-art, various swarm intelligence-based optimization algorithms are claimed to be better based on their arbitrary comparisons with different algorithms in different classes, e.g., swarm intelligence-based algorithms are compared with genetic algorithms. Unfortunately, they are not compared within their own class algorithms. Therefore, it is challenging to select an appropriate swarm intelligence-based algorithm for NARM. This article aims at filling this gap by conducting an exhaustive multi-aspect analysis of four popular swarm intelligence-based optimization algorithms (MOPAR, MOCANAR, ACO-R and MOB-ARM) with four real-world datasets and six major metrics and objectives: performance time, the number of rules, support, confidence, comprehensibility, and interestingness. In our analysis, the MOPAR algorithm produces a low number of rules and shows high values of confidence, comprehensibility, and interestingness. The MOCANAR algorithm provides satisfactory results with respect to all six parameters across all the data sets. The ACO-R algorithm produces high-quality rules but needs parameter modification for a large number of attributes in datasets, and the MOB-ARM algorithm is way slower than the other three algorithms.

Keywords: swarm intelligence optimization, association rule mining, Machine learning, numerical association rule mining

Suggested Citation

Kaushik, Minakshi, Swarm-Intelligence Algorithms for Mining Numerical Association Rules: An Exhaustive Multi-Aspect Analysis of Performance Assessment Data. Available at SSRN: https://ssrn.com/abstract=4399331 or http://dx.doi.org/10.2139/ssrn.4399331

Minakshi Kaushik (Contact Author)

Tallinn University of Technology (TUT) ( email )

Ehitajate tee 5
Tallinn, 12618
Estonia

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