Bonfring International Journal of Software Engineering and Soft Computing

Online ISSN: 2277-5099 | Print ISSN: 2250-1045 | Frequency: 4 Issues/Year

Impact Factor: 0.375 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)

A Dynamic Memory Adaptive Approach to Mine Frequent Pattern in Large Scale Databases

C. Vinothini and M. Kalimuthu

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Abstract:

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 is a 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.

Keywords: Frequent Patterns, Transcation Database, Apriori Algorithm, Association Rule, FP Tree

Volume: 2 | Issue: Special Issue on Communication Technology Interventions for Rural and Social Development

Pages: 01-05

Issue Date: February , 2012