Set Representation for Rule Generation Algorithms

The task of mining the association rule has become one most widely used discovery pattern methods in Knowledge Discovery Databases (KDD). One such is to represent itemset memory. representation largely depend on type data structure that for storing them. Computing process im- pacts memory and time requirement itemset. With increase dimensionality datasets, large volume datasets will be difficult since all these itemsets cannot placed main As an greatly affects efficiency association, a compact compress needed. In this paper, set introduced which more cost efficient. Bitmap takes byte element but uses bit. being incorporated Apriori Algorithm. Set also tested different generation algorithms. complexities algorithms using are compared terms execution.