A Rational Approach to Improve Access Time of Apriori Algorithm by Applying Inner Join in a Arm to Redefining Fis in Textual Data

Abstract

Now the Days Data mining is observed as most demanding field in current era and its scope and contribution in research is beyond the expectation for next upcoming decades. Among the various techniques of data mining, Association Rule Mining (ARM) (8) plays a remarkable role and Apriori Algorithm is one of the most popular method to define frequent item sets(FIS) over huge amount of transactional data sets. Now, we are going to re-evaluate access time which consumes in scanning of database again and again for n-times looking for n-element FIS. Here, we conclude and compare our proposed New-Apriori Algorithm with the original Apriori algorithm (5) through experimental result to calculating frequent items on numerous groups of transactions with minimal support, for both Apriori and New-Apriori to improve in its performance by reducing the scan time, spent on accessing the database by 61.65% to 73.65% for uniform support and 81% for variable support.