期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：63
期号：1
出版社：Journal of Theoretical and Applied
摘要：Association rule mining has been proposed for market basket analysis and to predict customer purchasing/spending behaviour by analyzing the frequent itemsets in a large pool of transactions. Finding frequent itemsets from a very large and dynamic dataset is a time consuming process. Several sequential algorithms have contributed to frequent pattern generation. Most of them face problems of time and space complexities and do not support incremental mining to accommodate change in customer purchase behaviour. To reduce these complexities researchers propose partitioned and parallel approaches; but they are compromising on anyone of these. An interactive and adaptive partitioned incremental mining algorithm with four level filtering approaches for frequent pattern mining is proposed here. It prepares incremental frequent patterns, without generating local frequent itemsets in less time and space complexities and is efficiently applicable to both sequential and parallel mining.
