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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 2    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 2,February 2012
An Efficient Parallel Approach for Frequent Itemset Mining of Incremental Data
Full Text(PDF, )  PP.175-179  
Author(s)
Mrs. Chetashri Bhadane, Dr. Ketan Shah, Mrs. Prajakta Vispute
KEYWORDS
Apriori , FUFP Algorithms, FP Tree Algorithm, Frequent Itemset, IMBT Structure, Incremental Data Mining, Parallel Data Mining,.
ABSTRACT
Frequent itemset mining is the essential step of data mining process. Further frequent itemset is a primary data obligatory for association rule mining. The Apriori and FP tree are conventional algorithms for mining frequent itemset and envisaging association rules based on it for knowledge discovery. The process of updating database continuously is known as incremental data mining. In real life, database updates recurrently where exactly conventional algorithms perform incompetently. If we could use the previous analysis to incrementally mine the frequent itemset from the updated database, the mining process would become more efficient and cost of mining process would be minimized. In this research, we propose a novel incremental mining scheme with a parallel approach for discovering frequent itemset. It uses a data structure called IMBT. It is a Incremental Mining Binary Tree which is used to record the itemset in an efficient way. Furthermore, our approach needs not to predetermine the minimum support threshold and scans the database only once.
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