Paper Title:
A Novel Efficient Classification Algorithm Based on Class Association Rules
  Abstract

A novel classification algorithm based on class association rules is proposed in this paper. Firstly, the algorithm mines frequent items and rules only in one phase. Then, the algorithm ranks rules that pass the support and confidence thresholds using a global sorting method according to a series of parameters, including confidence, support, antecedent cardinality, class distribution frequency, item row order and rule antecedent length. Classifier building is based on rule items that do not overlap in the training phase and rule items that each training instance is covered by only a single rule. Experimental results on the 8 datasets from UCI ML Repository show that the proposed algorithm is highly competitive when compared with the C4.5,CBA,CMAR and CPAR algorithms in terms of classification accuracy and efficiency. This algorithm can offer an available associative classification technique for data mining.

  Info
Periodical
Chapter
Chapter 1: Transportation & Service Science
Edited by
Robin G. Qiu and Yongfeng Ju
Pages
106-110
DOI
10.4028/www.scientific.net/AMM.135-136.106
Citation
S. J. Zhang, Q. Zhou, "A Novel Efficient Classification Algorithm Based on Class Association Rules", Applied Mechanics and Materials, Vols. 135-136, pp. 106-110, 2012
Online since
October 2011
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Price
$32.00
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