Prediction and Analysis on COVID-19 Using Positive and Negative Association Rule Mining

Chakraborty, Suman; Biswas, Sudarsan; Debnath, Sourav

doi:10.1007/978-981-16-1543-6_1

Cited by 1 publication

(4 citation statements)

References 12 publications

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“…Confdence and minimum correlation analyses are necessary to compute with two times, then the total computation is 2 * c 2 n � n * (n − 1). In addition, the time complexity of steps ( 6) to (18) is O(1), then the total time complexity is O(n 2 ), indicating that the improved approach does not increase extra time consumption.…”

Section: Pnars Mining Algorithm With Minimum Correlation Andmentioning

confidence: 99%

“…As an important technique in data mining, association rule analysis can extract implicit relations among data items that occur frequently together in many felds [13,[15][16][17][18]. Agrawal and Imieliński [19] frst proposed the Apriori algorithm by iteratively generating candidate itemsets in a database in 1993.…”

Section: Pnars Mining Techniquesmentioning

confidence: 99%

“…If corr(A, B) ≥ min_corr and the rule's confdence is greater than its own minimum confdence threshold, the algorithm generates rules like A ⇒ B and ¬A ⇒ ¬B (line (9) to line (10)). Otherwise, if corr(A, B) ≤ − min_corr, the rules like ¬A ⇒ B and A ⇒ ¬B are generated according to their support diference (line (12) to line (18)). In the end, line (21) returns the results and ends the whole algorithm.…”

Section: Pnars Mining Algorithm With Minimum Correlation Andmentioning

confidence: 99%

“…Similarly, lines ( 7), (10), and ( 13) are used to process different types of rules. Line (17) merges the two sets to PNARs, and line (18) returns the results and ends the whole algorithm. As can be seen from the PNARs_P algorithm, most of the time is taken by the scanning of rules set in PARs and NARs.…”

Section: Rules Pruning Algorithm For Pnarsmentioning

confidence: 99%

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An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Xiuguo,

Shengyong

2023

International Journal of Intelligent Systems

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Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

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Section: Pnars Mining Algorithm With Minimum Correlation Andmentioning

confidence: 99%