Classification Based on Predictive Association Rules of Incomplete Data

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Jeonghun Yoon, Dae-Won Kim

Published: 2012, Last Modified: 07 Nov 2025IEICE Trans. Inf. Syst. 2012EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Classification based on predictive association rules (CPAR) is a widely used associative classification method. Despite its efficiency, the analysis results obtained by CPAR will be influenced by missing values in the data sets, and thus it is not always possible to correctly analyze the classification results. In this letter, we improve CPAR to deal with the problem of missing data. The effectiveness of the proposed method is demonstrated using various classification examples.