Association Rule mining is one of the important and most popular data mining technique. It extracts interesting correlations, frequent patterns and associations among sets of items in the transaction databases or other data repositories. Most of the existing algorithms require multiple passes over the database for discovering frequent patterns resulting in a large number of disk reads and placing a huge burden on the input/output subsystem. In order to reduce repetitive disk read, a novel method of top down approach is proposed in this paper. The improved version of Apriori Algorithm greatly reduces the data base scans and avoids generation of unnecessary patterns which reduces data base scan, time and space consumption.