Performance Evaluation between Apriori and Improved Apriori

With massive amounts of data being collected and stored, many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision making processes such as marketing, catalog design etc.. In this respect Association rule mining is considered as one of the most important and well researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories.. This paper presents a comparison between classical frequent pattern mining algorithms that use candidate set generation and test the algorithms without candidate set generation. Here we present three algorithms Apriori, and Improved Apriori. ----------------------------------------------------------------------***------------------------------------------------------------------------