An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

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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Bibliographic Details
Main Authors: M. Sinthuja, S. Pravinthraja, B K Dhanalakshmi, H L Gururaj, Vinayakumar Ravi, G Jyothish Lal
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-03-01
Series:Journal of Safety Science and Resilience
Subjects:
Clustering
Data mining
Frequent itemset mining
Linear prefix tree
Maximal frequent itemset mining
Online Access:http://www.sciencedirect.com/science/article/pii/S2666449624000689
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