Using association rules for completing missing data

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.