A Comparative Analysis of Association Rule Mining Algorithms

Saxena, Akash; Rajpoot, Vikram

doi:10.1088/1757-899x/1099/1/012032

Cited by 26 publications

(16 citation statements)

References 8 publications

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“…However, we are interested in creating a web interface where FMAR is used to generate recommendations for users. In this scenario, we are particularly interested in employing more advanced algorithms to generate the association rules, such as AprioriTID [4][6], Apriori Hybrid [4] [6], AIS (Artificial Immune System) [7] and SETM [8]. We believe that using previous algorithms would help to further improve the performance and accuracy of FMAR.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the lift [5] is used to discover and exclude the weak rules that have high confidence, which can be calculated by dividing the confidence by the unconditional probability of the consequent (see Equation 5). Various algorithms are in place to create associations rules using previous metrics, such as Apriori [4][6], AprioriTID [4][6], Apriori Hybrid [4][6], AIS (Artificial Immune System) [7], SETM [8] and FP-growth (Frequent pattern) [4] [9]. In the next section, we provide more details about how we use these metrics to find the association rules used to improve the prediction time in the recommender system.…”

Section: Association Rulesmentioning

confidence: 99%

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Speeding Up Recommender Systems Using Association Rules

Kannout,

Nguyen,

Grzegorowski

2022

Preprint

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Recommender systems are considered one of the most rapidly growing branches of Artificial Intelligence. The demand for finding more efficient techniques to generate recommendations becomes urgent. However, many recommendations become useless if there is a delay in generating and showing them to the user. Therefore, we focus on improving the speed of recommendation systems without impacting the accuracy. In this paper, we suggest a novel recommender system based on Factorization Machines and Association Rules (FMAR). We introduce an approach to generate association rules using two algorithms: (i) apriori and (ii) frequent pattern (FP) growth. These association rules will be utilized to reduce the number of items passed to the factorization machines recommendation model. We show that FMAR has significantly decreased the number of new items that the recommender system has to predict and hence, decreased the required time for generating the recommendations. On the other hand, while building the FMAR tool, we concentrate on making a balance between prediction time and accuracy of generated recommendations to ensure that the accuracy is not significantly impacted compared to the accuracy of using factorization machines without association rules.

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Section: Discussionmentioning

confidence: 99%

Section: Association Rulesmentioning

confidence: 99%

Speeding Up Recommender Systems Using Association Rules

Kannout,

Nguyen,

Grzegorowski

2022

Preprint

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“…The "leverage" chart in Figure (10) is a bar graph that presents the leverage metric for various association rules derived from a dataset. Each bar in the chart corresponds to an association rule, speci cally showing the leverage value for the antecedent in that rule.…”

Section: End Formentioning

confidence: 99%

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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“…Association rule mining (ARM) is used to define the relation between a large number of data objects. [13] 5.…”

Section: Association Rule Miningmentioning

confidence: 99%

The Role of Data Stream Mining in Improving Self-Medication Practices in the Syrian Arab Republic

Abood

2023

Fuzzy Systems and Data Mining IX

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Self-medication is a widespread practice throughout the world, especially in the Syrian Arab Republic. However, if done incorrectly, this method might be harmful. Data stream mining, which involves analyzing vast amounts of data from multiple sources, has proven to be an effective technique for enhancing self-medication practices. The role of data stream mining, and how it may evaluate information from various sources like social media, electronic health records, pharmacy sales data, sensor-based medical devices, etc. have been explored for the research. Healthcare professionals in Syria can improve patient outcomes and safety related to self medication by employing data stream mining to uncover important information about patient behaviors, drug effectiveness, and adverse events. In this paper, a strategy for generating evidence-based medical data utilizing data stream mining techniques is suggested. Here, methods including association rule mining, categorization, and data clustering have been described. For future progression of this research, the information gap and other data collection-related problems need to be resolved. The implementation demonstrates that healthcare professionals can use a variety of data stream mining techniques to better understand drug usage patterns and spot opportunities while also learning about the prevalence of self-medication in different parts of the Syrian Arab Republic.

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A Comparative Analysis of Association Rule Mining Algorithms

Cited by 26 publications

References 8 publications

Speeding Up Recommender Systems Using Association Rules

Speeding Up Recommender Systems Using Association Rules

Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects

The Role of Data Stream Mining in Improving Self-Medication Practices in the Syrian Arab Republic

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