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9th International Database Engineering & Application Symposium (IDEAS'05)
Distribution-Based Synthetic Database Generation Techniques for Itemset Mining
Montreal, Canada
July 25-July 27
ISBN: 0-7695-2404-4
ASCII Text
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Ganesh Ramesh, Mohammed J. Zaki, William A. Maniatty, "Distribution-Based Synthetic Database Generation Techniques for Itemset Mining," Database Engineering and Applications Symposium, International, pp. 307-316, 9th International Database Engineering & Application Symposium (IDEAS'05), 2005.
 
 
BibTex
x
 
@article{ 10.1109/IDEAS.2005.22,
author = {Ganesh Ramesh and Mohammed J. Zaki and William A. Maniatty},
title = {Distribution-Based Synthetic Database Generation Techniques for Itemset Mining},
journal ={Database Engineering and Applications Symposium, International},
volume = {0},
year = {2005},
issn = {1098-8068},
pages = {307-316},
doi = {http://doi.ieeecomputersociety.org/10.1109/IDEAS.2005.22},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Database Engineering and Applications Symposium, International
TI - Distribution-Based Synthetic Database Generation Techniques for Itemset Mining
SN - 1098-8068
SP307
EP316
A1 - Ganesh Ramesh,
A1 - Mohammed J. Zaki,
A1 - William A. Maniatty,
PY - 2005
KW - null
VL - 0
JA - Database Engineering and Applications Symposium, International
ER -
 
Ganesh Ramesh, University of British Columbia
Mohammed J. Zaki, Rensselaer Polytechnic Institute
William A. Maniatty, State University of New York at Albany
The resource requirements of frequent pattern mining algorithms depend mainly on the length distribution of the mined patterns in the database. Synthetic databases, which are used to benchmark performance of algorithms, tend to have distributions far different from those observed in real datasets. In this paper we focus on the problem of synthetic database generation and propose algorithms to effectively embed within the database, any given set of maximal pattern collections, and make the following contributions:
  • 1. A database generation technique is presented which takes k maximal itemset collections as input, and constructs a database which produces these maximal collections as output, when mined at k levels of support. To analyze the efficiency of the procedure, upper bounds are provided on the number of transactions output in the generated database.
  • 2. A compression method is used and extended to reduce the size of the output database. An optimization to the generation procedure is provided which could potentially reduce the number of transactions generated.
  • 3. Preliminary experimental results are presented to demonstrate the feasibility of using the generation technique.
    • Citation:
    Ganesh Ramesh, Mohammed J. Zaki, William A. Maniatty, "Distribution-Based Synthetic Database Generation Techniques for Itemset Mining," ideas, pp.307-316, 9th International Database Engineering & Application Symposium (IDEAS'05), 2005
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