An Efficient Technique for mining Association rules using Enhanced Apriori Algorithm A Literature survey

Abstract

At present Data mining has a lot of e-Commerce applications. The key problem in this is how to find useful hidden patterns for better business applications in the retail sector. For the solution of those problems, The Apriori algorithm is the most popular data mining approach for finding frequent item sets from a transaction dataset and derives association rules. Association Rules are the discovered knowledge from the data base. Finding frequent item set (item sets with frequency larger than or equal to a user specified minimum support) is not trivial because of its combinatorial explosion. Once item sets are obtained, it is straightforward approach to generate association rules with confidence value larger than or equal to a user specified minimum confidence value. Apriori uses bottom up strategy. It is the most famous and classical algorithm for mining frequent patterns. Apriori algorithm works on categorical attributes. Apriori uses breadth first search