An Optimized Apriori-Based Frequent Itemset Mining Approach Using Apache Spark for Large-Scale Datasets.
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| Title: | An Optimized Apriori-Based Frequent Itemset Mining Approach Using Apache Spark for Large-Scale Datasets. |
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| Authors: | ELAVARASI, D.1 d.ilavarasi@outlook.com, KAVITHA, R.2 |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1071-1078. 8p. |
| Subjects: | Apriori algorithm, Association rule mining, Big data, Scalability, Data mining |
| Abstract: | Frequent itemset mining, the foundation of association rule mining, is a widely used technique for extracting valuable patterns from large corporate datasets. Among the early algorithms, the Apriori algorithm is well-known, yet it suffers from two major limitations: repeated dataset scans and the need to generate all candidate itemsets prior to support calculation. These drawbacks significantly impact performance, particularly in large-scale and distributed environments. To address these challenges, we propose an enhanced approach, USAHFAPIM (Uplift Scale Apriori-Based High Frequent Association Pruning Item Sets Mining), that leverages the Apache Spark framework for efficient processing of massive datasets with minimal memory consumption. The approach introduces two key innovations. First, it extracts itemsets by dynamically assessing input data, directly computing their support and confidence, which are used to calculate lift and determine strong associations. Second, it improves search efficiency by pruning redundant or duplicate data using a frequency-based filtering mechanism that reduces data loss. Through these mechanisms, USAHFAPIM enhances data analysis efficiency and significantly reduces execution time for large-scale and sparse datasets. Experimental results demonstrate that USAHFAPIM outperforms traditional algorithms such as Eclat, FP-Growth, and standard Apriori, achieving an accuracy of 94%, a precision of 93%, a recall of 92%, a false positive rate (FPR) of 0.08, and an execution time of 25-32 seconds at a minimum support threshold of 0.36%. These results confirm that USAHFAPIM is highly efficient and scalable for both dense and sparse datasets in big data environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131795 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Optimized Apriori-Based Frequent Itemset Mining Approach Using Apache Spark for Large-Scale Datasets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ELAVARASI%2C+D%2E%22">ELAVARASI, D.</searchLink><relatesTo>1</relatesTo><i> d.ilavarasi@outlook.com</i><br /><searchLink fieldCode="AR" term="%22KAVITHA%2C+R%2E%22">KAVITHA, R.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1071-1078. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Frequent itemset mining, the foundation of association rule mining, is a widely used technique for extracting valuable patterns from large corporate datasets. Among the early algorithms, the Apriori algorithm is well-known, yet it suffers from two major limitations: repeated dataset scans and the need to generate all candidate itemsets prior to support calculation. These drawbacks significantly impact performance, particularly in large-scale and distributed environments. To address these challenges, we propose an enhanced approach, USAHFAPIM (Uplift Scale Apriori-Based High Frequent Association Pruning Item Sets Mining), that leverages the Apache Spark framework for efficient processing of massive datasets with minimal memory consumption. The approach introduces two key innovations. First, it extracts itemsets by dynamically assessing input data, directly computing their support and confidence, which are used to calculate lift and determine strong associations. Second, it improves search efficiency by pruning redundant or duplicate data using a frequency-based filtering mechanism that reduces data loss. Through these mechanisms, USAHFAPIM enhances data analysis efficiency and significantly reduces execution time for large-scale and sparse datasets. Experimental results demonstrate that USAHFAPIM outperforms traditional algorithms such as Eclat, FP-Growth, and standard Apriori, achieving an accuracy of 94%, a precision of 93%, a recall of 92%, a false positive rate (FPR) of 0.08, and an execution time of 25-32 seconds at a minimum support threshold of 0.36%. These results confirm that USAHFAPIM is highly efficient and scalable for both dense and sparse datasets in big data environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20250807002887 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1071 Subjects: – SubjectFull: Apriori algorithm Type: general – SubjectFull: Association rule mining Type: general – SubjectFull: Big data Type: general – SubjectFull: Scalability Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: An Optimized Apriori-Based Frequent Itemset Mining Approach Using Apache Spark for Large-Scale Datasets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ELAVARASI, D. – PersonEntity: Name: NameFull: KAVITHA, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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