An Adaptive and bounded Approach to Mine Frequent Pattern in Large Scale Databases

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C. Vinothini
E. Meenachi


Frequent Patterns are very important in knowledge discovery and data mining process such as mining of association rules, correlations etc. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to solve association rule mining. In FP-tree isa compact representation of transaction database that contains frequency information of all relevant Frequent Patterns (FP) in a dataset. Mining association rules among items in a large database has been recognized as one of the most important data mining problems. An earlier approach proposes a model that is capable of mining in transactional database, but that approach is not capable of managing the problem of changing the memory dynamically. In order to solve this problem we have been proposed a hybrid of two algorithms that could be able to handle the dynamic change of memory, dynamic databases and also to solve the problem of association rule mining problems. So memory can be utilized effectively in large scale transaction database.


Index terms: Frequent Patterns, transcation database, Apriori algorithm, Association rule, FP tree


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