An Improved Eclat Algorithm based Association Rules Mining Method for Failure Status Information and Remanufacturing Machining Schemes of Retired Products

Wang, Lei; Guo, Yan; Guo, Yuyao; Xia, Xuhui; Zhang, Zelin; Cao, Jianhua

doi:10.1016/j.procir.2023.06.098

Cited by 4 publications

(1 citation statement)

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“…However, because Eclat algorithm takes a long time to find the intersection when there is a large amount of data, Zhang et al [ 12 ] used minwise hashing and estimator to quickly calculate the intersection size of multiple item sets, thus improving its efficiency. Based on the fact that Eclat consumes a large amount of memory space and computation time in data reading and computation under a large amount of data, and generates a large amount of redundant data in the process, Wang et al [ 13 ] improved the pruning strategy of Eclat, which reduced the generation of redundant frequent items, and improved the efficiency of the algorithm. In recent years, many researchers have improved on the basis of the above two algorithms.…”

Section: Introductionmentioning

confidence: 99%

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China

Wu,

Wen,

Zhu

2024

PLoS ONE

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Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

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Section: Introductionmentioning

confidence: 99%