Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Yedjour, Dounia; Yedjour, Hayat; Chouraqui, Samira

doi:10.1080/0954898x.2022.2137258

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…After decades of development, various solution strategies for association rule mining problems emerge in endlessly. For example, meta-heuristic-based approaches include ant colony optimization, artificial neural network, differential evolution, genetic algorithm, and particle swarm optimization [8][9][10][11][12]. Taking differential evolution as an example, Altay and Alatas proposed hybrid optimization and global search methods based on differential evolution and sine cosine algorithm, which can automatically adjust the appropriate interval of numerical valued attributes and mine without finding frequent itemsets, thus realizing the mining of numerical association rules.…”

Section: Introductionmentioning

confidence: 99%

Equipment Quality Information Mining Method Based on Improved Apriori Algorithm

Meng

Wang

et al. 2023

Journal of Sensors

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Equipment quality-related data contains valuable information. Data mining technology seems to be an efficient method for extracting knowledge from large amounts of data. In this paper, a general method for equipment quality information mining based on association rule is proposed for complex equipment. Due to the shortcomings of classical association rule mining algorithms such as long running time and high memory consumption, the candidate itemset generation process is optimized, and an improved Apriori algorithm is proposed. Taking five experimental data sets as the object, the performance of the algorithms is tested using time complexity and spatial complexity as evaluation criteria. Comparative experiments show that the improved algorithm had advantages. To further implement data processing and information representation, a matrix-based strong association rule extraction algorithm was proposed. Taking a certain type of equipment as an example, a simulation experiment was conducted using the method proposed in this article in reliability test data sets, and some interesting knowledge was obtained through mining, verifying the effectiveness of the method. The research in this article seems promising with respect to improving the scientific level of equipment support.

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