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There are lots of data mining tasks such as association rule, clustering, classification, regression and others. Among these tasks association rule mining is most prominent. One of the most popular approaches to find frequent item set in a given transactional dataset is Association rule mining. Frequent pattern mining is one of the most important tasks for discovering useful meaningful patterns from large collection of data. The FP Growth algorithm is currently one of the fastest approaches to frequent item set mining. This paper proposed an efficient and improved FP Tree algorithm which used a projection method to reduce the database scan and save the execution time. The advantage of PFP Tree is that it takes less memory and time in association mining. Experimental result showed that the improved PFP Tree algorithm performs faster than FP growth Tree algorithm and partition projection algorithm. It is more efficient and scalable in the case of large volume of data. The effectiveness of the method has been justified over a sample our one super market database.

An FP Tree based Approach for Extracting Frequent Pattern from Large Database by Applying Parallel and Partition Projection