Item set extraction without using constraints

Abstract

This paper presents the Item set extraction without using constraints, 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, It 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 index structure can be efficiently exploited by different item set extraction algorithms. In particular, data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories. 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 low supports) better than, state-of-the-art algorithms accessing data on flat file.