Business Intelligence

Graph Based Approach for Finding Frequent Itemsets to Discover Association Rules

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Executive Summary

The discovery of association rules is an important task in data mining and knowledge discovery. Several algorithms have been developed for finding frequent itemsets and mining comprehensive association rules from the databases. The efficiency of these algorithms is a major issue since a long time and has captured the interest of a large community of researchers. This paper presents a new approach that can mine frequent itemset or patterns in less time and in a straight forward way. Majority of the algorithms developed for finding frequent itemsets scan the database repeatedly and are based on the concept of minimum threshold support value.

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