期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：5
页码：1871-1875
出版社：TechScience Publications
摘要：Most of the research activities in association rule mining focuses on defining efficient algorithms for item set extraction. To reduce the computational complexity of item set extraction, support constraint is enforced on the extracted item sets.The IMine index structure can be efficiently exploited by different item set extraction algorithms. This paper presents the IMine index, a general and compact structure which provides tight integration of item set extraction in a relational DBMS. Since no constraint is enforced during the index creation phase, IMine provides a complete representation of the original database. To reduce the I/O cost, data accessed together during the same extraction phase are clustered on the same disk block. The IMine index has been integrated into the PostgreSQL DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large data sets. Item set mining supported by the IMine index shows performance always comparable with, and often (especially for lowsupports) better than, state-of-theart algorithms accessing data on flat file.
