Variable-length differential evolution for numerical and discrete association rule mining

Uroš Mlakar (Author), Iztok Fister (Author), Iztok Fister (Author)

Abstract

This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.

Keywords

diferencialna evolucija;podatkovno rudarjenje;predstavitev rešitev;association rule mining;differential evolution;data mining;variable-lenght solution representation;green AI;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
UDC:	004.8
COBISS:	181528835
ISSN:	2169-3536
Views:	265
Downloads:	2
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	diferencialna evolucija;podatkovno rudarjenje;predstavitev rešitev;
Type (COBISS):	Article
Pages:	str. 4239-4254
Volume:	ǂVol. ǂ12
Chronology:	29 Dec. 2023
DOI:	10.1109/ACCESS.2023.3348408
ID:	22427607