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Looking at the Class Associative Classification Training Algorithm

Thabtah, Fadi Abdeljaber, Mahmood, Qazafi and McCluskey, T.L. (2008) Looking at the Class Associative Classification Training Algorithm. Fifth International Conference on Information Technology: New Generations, 2008. ITNG 2008.. pp. 426-431.

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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

Item Type: Article
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
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Depositing User: Briony Heyhoe
Date Deposited: 27 Jan 2009 11:22
Last Modified: 20 Jul 2011 10:13
URI: http://eprints.hud.ac.uk/id/eprint/3216

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