A Study on Interestingness Measures for Associative Classifiers
- Mojdeh Jalali-Heravi
- Osmar R. Zaiane, University of Alberta (Database)
Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Support and confidence are the de-facto “interestingness measures” used for discovering relevant association rules. The supportconfidence framework has also been used in most, if not all, associative classifiers. Although support and confidence are appropriate measures for building a strong model in many cases, they are still not the ideal measures and other measures could be better suited. There are many other rule interestingness measures already used in machine learning, data mining and statistics. This work focuses on using 53 different objective measures for associative classification rules. A wide range of UCI datasets are used to study the impact of different “interestingness measures” on different phases of associative classifiers based on the number of rules generated and the accuracy obtained. The results show that there are interestingness measures that can significantly reduce the number of rules for almost all datasets while the accuracy of the model is hardly jeopardized or even improved. However, no single measure can be introduced as an obvious winner.
Citation
M. Jalali-Heravi, O. Zaiane. "A Study on Interestingness Measures for Associative Classifiers". ACM-SAC Conference Track on Data Mining, Sierre, Switzerland, pp 1040-1047, March 2010.| Keywords: | |
| Category: | In Conference |
| Web Links: | ACM Digital Library |
BibTeX
@incollection{Jalali-Heravi+Zaiane:10,
author = {Mojdeh Jalali-Heravi and Osmar R. Zaiane},
title = {A Study on Interestingness Measures for Associative Classifiers},
Pages = {1040-1047},
booktitle = {ACM-SAC Conference Track on Data Mining},
year = 2010,
}Last Updated: January 15, 2020Submitted by Sabina P
