IMine: Index Support for Item Set Mining

• Published in: IEEE transactions on knowledge and data engineering Vol. 21; no. 4; pp. 493 - 506
• Main Authors: Baralis, E., Cerquitelli, T., Chiusano, S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Association rules; Blocking; Categories; Clustering algorithms; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Costs; Data base management systems; Data mining; Data processing. List processing. Character string processing; Data structures; Exact sciences and technology; Extraction; Imines; Indexes; Indexing; Itemset Extraction; Memory organisation. Data processing; Mining; Relational data bases; Relational databases; Scalability; Software; Studies; Transaction databases; Data Mining; Itemset Extraction; Indexing; Content access; Data analysis; Scalability; Database; Very large databases; Data distribution; Information extraction; Data mining; item set extraction; Database management system
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2008.180

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 structure can be efficiently exploited by different item set extraction algorithms. In particular, IMine data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories. 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 datasets. Item set mining supported by the IMine index shows performance always comparable with, and sometimes better than, state of the art algorithms accessing data on flat file.