A pseudo-nearest-neighbor approach for missing data recovery on Gaussian random data sets

摘要

Missing data handling is an important preparation step for most data discrimination or mining tasks. Inappropriate treatment of missing data may cause large errors or false results. In this paper, we study the effect of a missing data recovery method, namely the pseudo-nearest-neighbor substitution approach, on Gaussian distributed data sets that represent typical cases in data discrimination and data mining application. The error rate of the proposed recovery method is evaluated by comparing the clustering results of the recovered data sets to the clustering results obtained on the originally complete data sets.