A Novel Rule Weighting Approach in Classification Association Rule Mining

Authors:
Yanbo J. Wang;Qin Xin;Frans Coenen
Affiliations:
-;-;-
Venue:
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Year:
2007

Citing 0
Cited 4

Hierarchical associative classifier (HAC) for malware detection from the large and imbalanced gray list

Journal of Intelligent Information Systems
Associative classification in the prediction of tuberculosis

Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Classification based on specific rules and inexact coverage

Expert Systems with Applications: An International Journal
CAR-NF: A classifier based on specific rules with high netconf

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classification Association Rule Mining (CARM) is a recent Classification Rule Mining approach that builds an Association Rule Mining based classifier using Classification Association Rules (CARs). Regardless of which particular CARM algorithm is used, a similar set of CARs is always generated from data, and a classifier is usually presented as an ordered CAR list, based on a selected rule ordering strategy. In the past decade, a number of rule ordering strategies have been introduced that can be categorized under three headings: (1) support-confidence, (2) rule weighting, and (3) hybrid. In this paper, we propose an alternative rule-weighting scheme, namely CISRW (Class-Item Score based Rule Weighting), and develop a rule-weighting based rule ordering mechanism based on CISRW. Subsequently, two hybrid strategies are further introduced by combining (1) and CISRW. The experimental results show that the three proposed CISRW based/related rule ordering strategies perform well with respect to the accuracy of classification.