Using Association Rules for Completing Missing Data

Authors:
Chih-Hung Wu;Chian-Huei Wun;Hung-Ju Chou
Affiliations:
National University of Kaohsiung, Kaohsiung, Taiwan;National Sun Yat-Sen University, Kaohsiung, Taiwan;National University of Kaohsiung, Kaohsiung, Taiwan
Venue:
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Year:
2004

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Abstract

We present in this paper a new method for completing missing data using the concept of association rules. The basic idea is that association rules describe the dependency relationships among data entries in a dataset where all data, including the missing ones, should hold the similar relationships. For a missing datum, we guess its possible value according to related association rules. A new completing procedure and a new evaluation function are developed and presented. The evaluation function is scored according to the support, confidence, and lift of association rules, which reasonably reflects the dependency relationships among existing and missing data. Experimental results show that our method is feasible in completing some incomplete datasets.