AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM

Abstract

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.