K-Medoid Clustering Shows Negative Impact In Missing Data Imputation

Provided by: Creative Commons
Topic: Big Data
Format: PDF
Missing data are a common problem to be solved in the field of research. Missing data is a technique of imputing missing values from the known available values. The imputed value should be checked for accuracy. Missing data imputation imputes the missing values from the known values. Rather than imputing from the whole dataset, imputation techniques are applied in the clusters generated by using clustering algorithm. In this paper, k-medoid clustering is used. But when compared the results in terms of accuracy, it seems that k-medoid clusters are not suited for missing data Imputation.

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