Finding a unique Association Rule Mining algorithm based on data characteristics

This research compares the performance of three popular association rule mining algorithms, namely apriori, predictive apriori and tertius based on data characteristics. The accuracy measure is used as the performance measure for ranking the algorithms. A wide variety of association rule mining algo...

Full description

Saved in:
Bibliographic Details
Main Authors: Mazid, M.M., Shawkat Ali, A.B.M., Tickle, K.S.
Format: Conference Proceeding
Language:English
Subjects:
Association rules
Data engineering
Data mining
Informatics
Itemsets
Machine learning
Machine learning algorithms
Prediction algorithms
Taxonomy
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research compares the performance of three popular association rule mining algorithms, namely apriori, predictive apriori and tertius based on data characteristics. The accuracy measure is used as the performance measure for ranking the algorithms. A wide variety of association rule mining algorithms can create a time consuming problem for choosing the most suitable one for performing the rule mining task. A meta-learning technique is implemented for a unique selection from a set of association rule mining algorithms. On the basis of experimental results of 15 UCI data sets, this research discovers statistical information based rules to choose a more effective algorithm.
DOI:10.1109/ICECE.2008.4769340