An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering
The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently. This is the case with Association Rules, a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns. Althoug...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Journal of Safety Science and Resilience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666449624000689 |
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| author | M. Sinthuja S. Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal |
| author_facet | M. Sinthuja S. Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal |
| author_sort | M. Sinthuja |
| collection | DOAJ |
| description | The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently. This is the case with Association Rules, a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns. Although various approaches for extracting frequent itemset (prior step before mining association rules) in extremely large databases have been presented, the high computational cost and shortage of memory remain key issues to be addressed while processing enormous data. The objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent itemset. The performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max algorithm. Experimentation was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption. |
| format | Article |
| id | doaj-art-cb3aca0042ae41348d895115ca88ca79 |
| institution | Kabale University |
| issn | 2666-4496 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Safety Science and Resilience |
| spelling | doaj-art-cb3aca0042ae41348d895115ca88ca792025-02-06T05:12:52ZengKeAi Communications Co., Ltd.Journal of Safety Science and Resilience2666-44962025-03-016193104An efficient and resilience linear prefix approach for mining maximal frequent itemset using clusteringM. Sinthuja0S. Pravinthraja1B K Dhanalakshmi2H L Gururaj3Vinayakumar Ravi4G Jyothish Lal5Department of ISE M.S.Ramaiah Institute of Technology, Bengaluru; Visvesvaraya Technological University, Belagavi, Karnataka, 590018, IndiaDepartment of CSE, Presidency University, Bengaluru, IndiaDepartment of Computer Science and Engineering, BMS institute of Technology & Management, Bengaluru; Visvesvaraya Technological University, Belagavi, Karnataka, 590018, IndiaDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, IndiaCenter for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; Corresponding author.Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaThe numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently. This is the case with Association Rules, a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns. Although various approaches for extracting frequent itemset (prior step before mining association rules) in extremely large databases have been presented, the high computational cost and shortage of memory remain key issues to be addressed while processing enormous data. The objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent itemset. The performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max algorithm. Experimentation was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption.http://www.sciencedirect.com/science/article/pii/S2666449624000689ClusteringData miningFrequent itemset miningLinear prefix treeMaximal frequent itemset mining |
| spellingShingle | M. Sinthuja S. Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering Journal of Safety Science and Resilience Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining |
| title | An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| title_full | An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| title_fullStr | An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| title_full_unstemmed | An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| title_short | An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| title_sort | efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering |
| topic | Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining |
| url | http://www.sciencedirect.com/science/article/pii/S2666449624000689 |
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