A Survey on metaheuristic nature inspired computations used for Mining of Association Rule, Frequent Itemset and High Utility Itemset

Logeswaran, K.; Andal, R. K. S.; Ezhilmathi, S. T.; Khan, Azam; Suresh, P.; Kumar, K. R. Prasanna

doi:10.1088/1757-899x/1055/1/012103

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…Mining of Association rule, Frequent Itemset, and High Utility Itemset are strongly interrelated and developing in the eld of Data Mining [9]. The Apriori algorithm is foundational in the eld of Association Rule Mining.…”

Section: A Apriori Algorithmmentioning

confidence: 99%

“…In practical terms, conviction tells us how often the consequent would be false if the antecedent were true. The conviction value in gure (9), approaches in nity (replaced by the maximum nite conviction value in the dataset, as per the code) when the con dence is 1 (i.e., the rule is always true). Color Scheme ranges from yellow to green for lower values to dark blue for higher values.…”

Section: End Formentioning

confidence: 99%

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Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects

Eldesouky,

Raslan

2024

Preprint

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Focusing on identifying hidden patterns and dependencies between software defects that are difficult to detect using traditional analysis methods, this study employs Association Rule Mining (ARM) to analyze over 140,000 open-source GitHub issues. By leveraging ARM, we have been able to extract explicit association rules that illustrate the interrelations among various issue attributes such as labels and release versions. Our findings indicate strong, meaningful associations that equip developers and quality assurance teams with the information necessary to strategically prioritize issues. This prioritization significantly influences bug fixing and system enhancements. Offering a marked improvement over conventional analysis techniques, ARM provides a clearer and more precise understanding of defect dynamics, thereby enhancing the efficiency and effectiveness of software development practices.

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Section: A Apriori Algorithmmentioning

confidence: 99%

Section: End Formentioning

confidence: 99%

Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects

Eldesouky,

Raslan

2024

Preprint

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“…Where: m and Support_ count (E) respectively represent the number of data (the total number of events) and the number of times that e itemsets appear in management. B% of the management in management set B includes both E with e Q constraint and F with f Q constraint [15] .…”

Section: Application Of Association Rule Mining Algorithmmentioning

confidence: 99%

Application of association rule mining algorithm in the design of information-based teaching management system

2023

Atlantis Highlights in Engineering

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On this basis, combined with the characteristics of computer-aided instruction, a data-based computer-aided instruction management system is designed and implemented. On this basis, a data-based education management system is proposed, which is combined with the rule mining algorithm. On this basis, a data mining management system based on association rules is proposed. Association rule discovery algorithm can extract required information from massive association rules. The data obtained in this way can effectively manage the information resources of the university. This paper applies this method to my data requirements for implementing Kwalliso teaching from database to realize data-based teaching management, and analyzes the effectiveness of this system in the context of classroom questionnaires, customer interaction. Taking an information platform of a university as an example to test the performance of the system, the results show that the system can realize the course query function, has good client compatibility, can be applied to various browsers and operating systems, and has strong application and good data mining performance.

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“…3 Wireless Communications and Mobile Computing in the distributed energy database, and the conditions of strong association rules can be obtained [16,17].…”

Section: Confidence Calculation Of Higher Education Resourcementioning

confidence: 99%

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

Liu

Ren

et al. 2022

Wireless Communications and Mobile Computing

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The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

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A Survey on metaheuristic nature inspired computations used for Mining of Association Rule, Frequent Itemset and High Utility Itemset

Cited by 7 publications

References 28 publications

Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects

Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects

Application of association rule mining algorithm in the design of information-based teaching management system

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

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