Data Visualization Technology in Power Grid Planning and Government Affairs Based on Association Rules Algorithm

Li, Xiaoxia; Lou, Jinman; Huang, Guokang; Liu, Piao; Liang, Haobo

doi:10.1007/978-3-031-05237-8_71

Cited by 2 publications

(1 citation statement)

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“…Apriori association rules algorithm scans the transaction database by a layer-by-layer iterative method and then obtains frequent item sets. The main idea of generating frequent item sets is as follows [ 18 , 19 ]: first, scan the transaction database multiple times to generate candidate item sets of length k +1 from k frequent item sets; then, perform mining and judgment on k +1 candidate item sets to obtain k +1 Frequent item sets. According to the same method, until no more k -item sets are found, the algorithm execution ends.…”

Section: Association Rulementioning

confidence: 99%

A Data Mining Algorithm for Association Rules with Chronic Disease Constraints

Liu

Wang

Miao

et al. 2022

Computational Intelligence and Neuroscience

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The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China’s medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.

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Section: Association Rulementioning

confidence: 99%