Parimputation: From Imputation and Null-Imputation to Partially Imputation

process of machine learning and data mining when certain values are missed. Among extant imputation techniques, kNN imputation algorithm is the best one as it is a model free and efficient compared with other methods. However, the value of k must be chosen properly in using kNN imputation. In particular, when some nearest neighbors are far from a missing data, the kNN imputation algorithms are often of low efficiency. In this paper, a new imputation framework is designed. The imputation uses the left or right nearest neighbor for a missing data in a given dataset. Furthermore, a parimputation (partially imputation) strategy is proposed for dealing with the issue of missing data imputation. Specifically, some missing data are imputed when there are some complete data in a small neighborhood of the missing data and, other missing data without imputation are given up in applications, such as data mining and machine learning.