An Efficient ARM Technique for Information Retrieval in Data Mining

Association rule mining is the one of the most important technique of the data mining. Its aim is to extract interesting correlations, frequent patterns and association among set of items in the transaction database. In association rule mining (ARM), there are several algorithms. FP growth is the classical and most efficient algorithm. In this paper, author considers data (Supermarket data) and tries to obtain the result using Weka data mining tool. Association rule algorithms are used to find out the best combination of different attributes in any data. Here author consider three association rule algorithms: Apriori Association Rule, FP-Growth association rule and Tertius Association Rule. Author compares the result of these three algorithms and presents the result. According to the result obtained using data mining tool author find that FP growth Association algorithm performs better than the Apriori association rule and Tertius Association Rule algorithms.

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