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A New Classification Based on Association Algorithm

Thabtah, Fadi Abdeljaber, Mahmood, Qazafi, McCluskey, T.L. and Abdel-Jaber, Hussein (2010) A New Classification Based on Association Algorithm. Journal of Information & Knowledge Management, 9 (1). pp. 55-64. ISSN 0219-6492

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Abstract

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

Item Type: Article
Subjects: 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: Thomas Mccluskey
Date Deposited: 22 Sep 2011 15:18
Last Modified: 08 Nov 2011 12:10
URI: http://eprints.hud.ac.uk/id/eprint/11505

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