Evaluation of Three Simple Imputation Methods for Enhancing Preprocessing of Data with Missing Values

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
One of the important stages of data mining is preprocessing, where the data is prepared for different mining tasks. Often, the real-world data tends to be incomplete, noisy and inconsistent. It is very common that the data are not obtainable for every observation of every variable. So the presence of missing variables is obvious in the data set. A most important task when preprocessing the data is, to fill in missing values, smooth out noise and correct inconsistencies.

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