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Looking at the class associative classification training algorithm

Mahmood, Qazafi, Thabtah, Fadi Abdeljaber and McCluskey, T.L. (2007) Looking at the class associative classification training algorithm. In: Proceedings of Computing and Engineering Annual Researchers' Conference 2007: CEARC’07. University of Huddersfield, Huddersfield, pp. 1-9.

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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 mergings during the learning step, and consequently results in huge saving in computational time and memory.

Item Type: Book Chapter
Uncontrolled Keywords: Associative Classification, Classification, Data Mining, Itemset, Training Phase
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 > Computing and Engineering Annual Researchers' Conference (CEARC)
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Graham Stone
Date Deposited: 23 Mar 2009 14:25
Last Modified: 28 Aug 2015 10:00
URI: http://eprints.hud.ac.uk/id/eprint/3705

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