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IMine: Index Support for Item Set Mining
April 2009 (vol. 21 no. 4)
pp. 493-506
ASCII Text
x 
 
Elena Baralis, Tania Cerquitelli, Silvia Chiusano, "IMine: Index Support for Item Set Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 4, pp. 493-506, April, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.180,
author = {Elena Baralis and Tania Cerquitelli and Silvia Chiusano},
title = {IMine: Index Support for Item Set Mining},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {21},
number = {4},
issn = {1041-4347},
year = {2009},
pages = {493-506},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.180},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - IMine: Index Support for Item Set Mining
IS - 4
SN - 1041-4347
SP493
EP506
EPD - 493-506
A1 - Elena Baralis,
A1 - Tania Cerquitelli,
A1 - Silvia Chiusano,
PY - 2009
KW - Data Mining
KW - Itemset Extraction
KW - Indexing
VL - 21
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Elena Baralis, Politecnico di Torino, Torino
Tania Cerquitelli, Politecnico di Torino, Torino
Silvia Chiusano, Politecnico di Torino, Torino
Web Extra: View Supplemental Material
This paper presents the IMine index, a general and compact structure which provides tight integration of itemset 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 itemset 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. Itemset 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.

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Index Terms:
Data Mining, Itemset Extraction, Indexing
Citation:
Elena Baralis, Tania Cerquitelli, Silvia Chiusano, "IMine: Index Support for Item Set Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 4, pp. 493-506, April 2009, doi:10.1109/TKDE.2008.180
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