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. 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.