A Classification Rules Mining Method based on Dynamic Rules' Frequency

Date

2015

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Computer Society

Type

Conference

Peer reviewed

Yes

Abstract

Rule based classification or rule induction (RI) in data mining is an approach that normally generates classifiers containing simple yet effective rules. Most RI algorithms suffer from few drawbacks mainly related to rule pruning and rules sharing training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed that utilises two thresholds to minimise the items search space. Whenever a rule is generated, DRI algorithm ensures that all candidate items' frequencies are updated to reflect the deletion of the rule’s training data instances. Therefore, the remaining candidate items waiting to be added to other rules have dynamic frequencies rather static. This enables DRI to generate not only rules with 100% accuracy but rules with high accuracy as well. Experimental tests using a number of UCI data sets have been conducted using a number of RI algorithms. The results clearly show competitive performance in regards to classification accuracy and classifier size of DRI when compared to other RI algorithms.

Description

Keywords

Classification Rules, Data Mining, Rule Induction, Dynamic, Experimental tests

Citation

Qabajeh, I., Chiclana, F. and Thabtah, F. (2015) A Classification Rules Mining Method based on Dynamic Rules' Frequency. Proceedings of the 12th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2015

Rights

Research Institute