Geo Map Visualization for Frequent Purchaser in Online Shopping Database Using an Algorithm LP-Growth for Mining Closed Frequent Itemsets
M. Sinthuja, N. Puviarasan, P. Aruna · Procedia Computer Science · 2018
Numerous frequent itemsets are explored using frequent itemset mining algorithm which contains redundant information. Fortunately, this issue is decreased to the mining of closed frequent itemsets. However, these approaches still have some performance bottlenecks like processing time and storage space. Moreover, new algorithms of closed frequent itemsets are presented. In this paper, the proposed closed frequent itemset (LP-Closed-tree) using Linear prefix growth method is introduced which is a powerful approach for mining frequent itemsets. It builds basic tree and mines frequent itemsets. The proposed LP-Closed-tree is implemented on real and dense database like online shopping database and chess and is evaluated with other existing algorithms. While using online shopping dataset, the frequent purchaser of the dataset is visualized using google map in geographical method. After comprehensive empirical appraisal it is found that the proposed LP-Closed-tree algorithms are faster in many cases. Moreover, these algorithms are known for relatively lesser consumption of time and memory in cases of large and dense database.