Abstract

The IMine index, a general and compact structure which provides tight integration of item set extraction during a relational DBMS. IMine provides an entire representation of the first database, Since no constraint is enforced during the index creation phase. To reduce the I/O cost, data accessed together during an equivalent extraction phase are clustered on an equivalent disk block. The IMine index structure are often efficiently exploited by different item set extraction algorithms. IMine data access method supports the LCM v.2 algorithms and FP-growth, but they will straightforwardly support the enforcement of varied constraint categories. It has been integrated into the Postgre SQL DBMS and exploits its physical level access methods. Experiments, run both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for giant data sets.Item set mining supported by the IMine index shows performance always comparable , and sometimes (especially for low supports) better than, state-of-the-art algorithms accessing data on file.