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Looking at the class associative classification training algorithm

By Qazafi Mahmood, Fadi Abdeljaber Thabtah and T.L. McCluskey

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

Topics: T1, QA75
Publisher: University of Huddersfield
Year: 2007
OAI identifier: oai:eprints.hud.ac.uk:3705

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