Bonfring International Journal of Industrial Engineering and Management Science
Online ISSN: 2277-5056 | Print ISSN: 2250-1096 | Frequency: 4 Issues/Year
Impact Factor: 0.541 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)
Mining Frequent Patterns in Large Scale Databases Using Adaptive FP-Growth Approach
Abstract:
Frequent Patterns (FPs) are extremely vital in knowledge discovery and data mining process, for instance, mining of association rules, correlations etc. Several existing incremental mining schemes are mostly Apriori-based, which are not easily adaptable to solve association rule mining. FP-tree is a compact representation of transaction database that includes frequency information of the entire relevant FPs in a dataset. Mining association rules in the midst of items in a large database is one of the most vital data mining problems. An earlier scheme proposed a model that is able to mine in transactional database, however that approach is not able to manage the problem of changing the memory dynamically. Hence, in order to solve this complication, here a hybrid of two algorithms is proposed that has the potential of handling the dynamic change of memory, dynamic databases and also to solve the complication of association rule mining problems. Hence, memory can be effectively utilized in large scale transaction database.
Keywords: Frequent Patterns, Transaction Database, Apriori Algorithm, Association Rule, FP Tree.
Volume: 7 | Issue: 2
Pages: 17-20
Issue Date: May , 2017
DOI: 10.9756/BIJIEMS.8326
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