Looking at the Class Associative Classification Training Algorithm

Fadi Thabtah, Qazafi Mahmood, Lee McCluskey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Associative classification (AC) is a branch in data mining that utilises association rule discovery methods in classification problems. In this paper, we propose a new training method called Looking at the Class (LC), which can be adapted by any rule-based AC algorithm. Unlike the traditional Classification based on Association rule (CBA) training method, which joins disjoint itemsets regardless of their class labels, our method joins only itemsets with similar class labels during the training phase. This prevents the accumulation of too many unnecessary merging during learning, and consequently results in huge saving (58%-91%) with reference of computational time and memory on large datasets.

Original languageEnglish
Title of host publicationProceedings of the Fifth International Conference on Information Technology
Subtitle of host publicationNew Generations - ITNG 2008
EditorsShahram Latifi
PublisherIEEE
Pages426-431
Number of pages6
ISBN (Print)0769530990, 9780769530994
DOIs
Publication statusPublished - 18 Apr 2008
EventInternational Conference on Information Technology: New Generations - Las Vegas, United States
Duration: 7 Apr 20089 Apr 2008
http://www.itng.info/OLD/2008Web/

Conference

ConferenceInternational Conference on Information Technology
Abbreviated titleITNG 2008
CountryUnited States
CityLas Vegas
Period7/04/089/04/08
Internet address

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Thabtah, F., Mahmood, Q., & McCluskey, L. (2008). Looking at the Class Associative Classification Training Algorithm. In S. Latifi (Ed.), Proceedings of the Fifth International Conference on Information Technology: New Generations - ITNG 2008 (pp. 426-431). [4492517] IEEE. https://doi.org/10.1109/ITNG.2008.250