An Efficient Count Based Transaction Reduction Approach for Mining Frequent Patterns

Abstract

Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it will take too much time to scan the database. To overcome these limitations, researchers have made a lot of improvements to the Apriori. This paper analyses the classical algorithm as well as some disadvantages of the improved Apriori and also proposed two new transaction reduction techniques for mining frequent patterns in large databases. In this approach, the whole database is scanned only once and the data is compressed in the form of a Bit Array Matrix. The frequent patterns are then mined directly from this Matrix. It also adopts a new count-based transaction reduction and support count method for candidates. Appropriate operations are designed and performed on matrices to achieve efficiency. All the algorithms are executed in 5% to 25% support level and the results are compared. Efficiency is proved through performance analysis.