期刊名称：Bonfring International Journal of Industrial Engineering and Management Science
印刷版ISSN：2250-1096
电子版ISSN：2277-5056
出版年度：2017
卷号：7
期号：2
页码：17-20
DOI：10.9756/BIJIEMS.8326
语种：English
出版社：Bonfring
摘要：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.
关键词：Frequent Patterns; Transaction Database; Apriori Algorithm; Association Rule; FP Tree.
