Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining

Altay, Elif Varol; Alataş, Bilal

doi:10.1007/s12652-019-01540-7

Cited by 38 publications

(10 citation statements)

References 25 publications

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“…This data-driven sales strategy can help the supermarket better understand the market demand, and timely adjust the inventory and display of goods in response to changes in the objective environment [3]. Through the analysis of objective realtime data, the supermarket can more accurately grasp the law of commodity sales, so as to guide the supermarket to conduct combined sales [5]. For example, at the food counter, supermarket data analysis found that 90% of customers also purchase bread and butter while purchasing milk.…”

Section: The Relationship Between Vegetable Itemsmentioning

confidence: 99%

The Sales Combination Strategy of Fresh Supermarket Vegetables Based on Apriori

Chen,

Sun,

Huang

et al. 2024

JAMC

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With the development of the times, the key issue faced by fresh food supermarkets is the challenge of preserving vegetable products and their vulnerability to time, resulting in inventory backlog and increased loss rates. Consumers place greater emphasis on freshness and traceability in the vegetables sold by fresh supermarkets. Studying the sales strategy of vegetables that need to be sold out on the same day in fresh supermarkets has high research value. This article is based on the background of question C in the National College Student Mathematical Modeling Competition of the 2023 Higher Education Society Cup. Python software is used to scan 878504 cumulative data from the supermarket over the past three years. Frequent itemsets are selected and combined with daily vegetable sales. Apriori algorithm is used to determine the daily sales combination. This article obtained a sales mix centered on 11 vegetable items, including Yunnan oilseed rape, brassica campestris, and Sichuan pepper.

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Section: The Relationship Between Vegetable Itemsmentioning

confidence: 99%

The Sales Combination Strategy of Fresh Supermarket Vegetables Based on Apriori

Chen,

Sun,

Huang

et al. 2024

JAMC

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“…In the context of stochastic population-based natureinspired algorithms, Evolutionary Algorithms (EAs), like Genetic Algorithms (GAs), and Swarm Intelligence (SI) based algorithms, like Particle Swarm Optimization (PSO), which were often utilized for rule mining tasks, have been adapted to mine numerical association rules efficiently [8], [9]. These algorithms encode potential solutions as chromosomes, allowing variation operators such as crossover and mutation to refine the rule population iteratively.…”

Section: Introductionmentioning

confidence: 99%

Variable-Length Differential Evolution for Numerical and Discrete Association Rule Mining

Mlakar,

Fister,

Fister

2024

IEEE Access

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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.

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“…Generally, for intelligent algorithms, the database is considered as a search space, and the algorithm -as an exploration strategy that aims to explore the search space and define the rules that maximize/ minimize an earlier defined fitness function that evaluates the rule quality based on its measures. Moreover, many researches deal with ARM as a multi-objective optimization problem [9], [10]. This idea has been motivated by the huge number of rules' quality measures introduced in various objective functions.…”

Section: Introductionmentioning

confidence: 99%

A Binary Grey Wolf Optimizer with Mutation for Mining Association Rules

Heraguemi,

Kamel,

Mafarja

2024

CSJM

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In this decade, the internet becomes indispensable in companies and people life. Therefore, a huge quantity of data, which can be a source of hidden information such as association rules that help in decision-making, is stored. Association rule mining (ARM) becomes an attractive data mining task to mine hidden correlations between items in sizeable databases. However, this task is a combinatorial hard problem and, in many cases, the classical algorithms generate extremely large number of rules, that are useless and hard to be validated by the final user. In this paper, we proposed a binary version of grey wolf optimizer that is based on sigmoid function and mutation technique to deal with ARM issue, called BGWOARM. It aims to generate a minimal number of useful and reduced number of rules. It is noted from the several carried out experimentations on well-known benchmarks in the field of ARM, that results are promising, and the proposed approach outperforms other nature-inspired algorithms in terms of quality, number of rules, and runtime consumption.

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Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining

Cited by 38 publications

References 25 publications

The Sales Combination Strategy of Fresh Supermarket Vegetables Based on Apriori

The Sales Combination Strategy of Fresh Supermarket Vegetables Based on Apriori

Variable-Length Differential Evolution for Numerical and Discrete Association Rule Mining

A Binary Grey Wolf Optimizer with Mutation for Mining Association Rules

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