Classification Based on Predictive Association Rules of Incomplete Data

Jeonghun YOON; Dae-Won KIM

doi:10.1587/transinf.E95.D.1531

Classification Based on Predictive Association Rules of Incomplete Data

Jeonghun YOON, Dae-Won KIM

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.5 pp.1531-1535

Publication Date: 2012/05/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.1531

Type of Manuscript: LETTER

Category: Artificial Intelligence, Data Mining

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Jeonghun YOON, Dae-Won KIM, "Classification Based on Predictive Association Rules of Incomplete Data" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 5, pp. 1531-1535, May 2012, doi: 10.1587/transinf.E95.D.1531.
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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E95.D.1531/_p

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@ARTICLE{e95-d_5_1531,
author={Jeonghun YOON, Dae-Won KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Classification Based on Predictive Association Rules of Incomplete Data},
year={2012},
volume={E95-D},
number={5},
pages={1531-1535},
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.},
keywords={},
doi={10.1587/transinf.E95.D.1531},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Classification Based on Predictive Association Rules of Incomplete Data
T2 - IEICE TRANSACTIONS on Information
SP - 1531
EP - 1535
AU - Jeonghun YOON
AU - Dae-Won KIM
PY - 2012
DO - 10.1587/transinf.E95.D.1531
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2012
AB - 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.
ER -