|
Title:
|
REALISTIC SYNTHETIC DATA FOR RULE MINING |
|
Author(s):
|
Colin Cooper , Michele Zito |
|
ISBN:
|
978-972-8924-40-9 |
|
Editors:
|
Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
|
Year:
|
2007 |
|
Edition:
|
Single |
|
Keywords:
|
association-rule mining, benchmarks, synthetic databases. |
|
Type:
|
Short Paper |
|
First Page:
|
140 |
|
Last Page:
|
144 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
We investigate the statistical properties of the databases generated by the IBM QUEST program. Motivated by the claim
(also supported by empirical evidence) that item occurrences in real life market basket databases follow a rather different
pattern, we propose an alternative model for generating artificial data. We claim that such a model is simpler than
QUEST and generates structures that are closer to real-life market basket data. |
|
|
|
|
|
|
