CBT-fi: Compact BitTable Approach for Mining Frequent Itemsets

Abstract

Frequent item-set mining is a data analysis method which is used to find the relationship between the different items in the given database. Plenty of research work and progress has been made over the decades due to its wider applications. Recently, BitTableFI and Index-BitTableFI approaches have been applied for mining frequent item-sets and results are significant. They use Bit Table as the base data structure and exploits the bit table both horizontally and vertically. However still needs simple and efficient approach for mining frequent itemsets from the given dataset. This paper introduces the Compact BitTable approach for mining frequent itemsets (CBT-fi) which clusters(groups) the similar transaction into one and forms a compact bit-table structure which reduces the memory consumption as well as frequency of checking the itemsets in the redundant transaction. Finally we present result, which shows the proposed algorithm has better than the existing algorithms.