A Novel Data Mining Method to Find the Frequent Patterns from Predefined Itemsets in Huge Dataset Using TMPIFPMM

Abstract-Association rule mining is one of the important data mining techniques. It finds correlations among attributes in huge dataset. Those correlations are used to improve the strategy of the future business. The core process of association rule mining is to find the frequent patterns (itemsets) in huge dataset. Countless algorithms are available in the literature to find the frequent itemsets. Most of the algorithms introduced in the literature finds all frequent itemsets for a given specified minimum support value. But in rare occasion, it is needed to check the occurrence of some predefined few frequent patterns in large dataset to improve the strategy of the future business. For this purpose, we previously introduced SIFPMM (Selective Itemsets Frequent Pattern Mining Method) method. FP-tree is one of the important methods for finding frequent patterns using two database scans. So this proposed TM-PIFPMM (Transaction Merging – Predefined Itemsets Frequent Pattern Mining Method) finds frequent patterns from predefined frequent itemsets using one database scan and it is compared with FP-tree and SIFPMM. The practical study of TM-PIFPMM proves that this method outperforms than FP-tree and SIFPMM. Keywords-Apriori, FP-tree, SIFPMM, TM-PIFPMM, Minimum Support