Abstract Details
128 / 2024-04-14 13:47:10
Research and Application of Association Rule Mining Algorithm for Electric Power Equipment Manufacturing
Electric power equipment,association rule mining,feature dimensionality reduction,pruning strategy
Final Paper
The electric power equipment (EPE) manufacturing industry is currently facing challenges related to technological dependency and insufficient automation, limiting improvements of the production efficiency and product quality. The objective of this paper is to propose an improved association rule mining algorithm to extract the potential relationships among production elements of EPE manufacturing. First, the Random Forest algorithm (RF) is employed for feature dimensionality reduction to enhance computational efficiency. Additionally, the k-means clustering algorithm is used for data discretization to better handle continuous and multi-modal data. Finally, a support penalty for low-frequency itemsets is introduced to improve the algorithm's adaptability and accuracy in EPE manufacturing environments. Experimental results indicate that the improved association rule mining algorithm outperforms traditional algorithms in rule generation efficiency, rule quality, and computational time. Furthermore, the algorithm could be used to analyze and identify defects and faults, etc., in EPE production lines to improve production efficiency and product quality.
Important Date
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Conference Date
Nov 10
2024
toNov 13
2024
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Nov 11 2024
Draft paper submission deadline
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Nov 19 2024
Registration deadline
Sponsored By
Xi’an Jiaotong Universit
Contact Information
Previous Conferences
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2022-11-24 Bangladesh Dhaka
2022 International Conference on Energy and Power Engineering -
2019-03-14 Bangladesh
2019 International Conference on Energy and Power Engineering
