A New Classification Based on Association Algorithm

Fadi Thabtah (), Qazafi Mahmood (), Lee McCluskey () and Hussein Abdel-Jaber ()
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Fadi Thabtah: MIS Department, Philadelphia University, Jordan
Qazafi Mahmood: Computing Department, Hubbersfield University, UK
Lee McCluskey: Computing Department, Hubbersfield University, UK
Hussein Abdel-Jaber: Computing Department, The World Islamic Sciences & Education University, Jordan

Journal of Information & Knowledge Management (JIKM), 2010, vol. 09, issue 01, 55-64

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.

Keywords: Association rule; classification; data mining; itemset; training phase (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219649210002486

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