Missing values may occur for several reasons and affects the quality of data, such as
malfunctioning of measurement equipment, changes in experimental design during data …

We propose a comparative study on single imputation techniques such as Mean, Median,
and Standard Deviation combined with k-NN algorithm. Training set with their corresponding …

The absence of some data values in any observed dataset has been a real hindrance to
achieving valid results in statistical research. This paper aimed at the missing data …

Abstract Missing Values Management is one of the challenges faced by Data Analysts.
Therefore, the creation of effective data models will be the right decision for missing data …

Missing data are condition when there are some missing values or empty entries on several
observations on data. It could inhibit statistical analysis process and might give a bias …

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 …

In data mining one important stage is pre-processing. In which there are different mining
tasks for it. In real world most of the data are noisy, inconsistent and incorrect. In fact, the …

Data preparation is an important step in mining incomplete data. To deal with this problem,
this paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or …

Many data mining algorithms cannot handle incomplete datasets where some data samples
are missing attribute values. To solve this problem, missing value imputation is usually …

In the realm of medical datasets, particularly when considering diabetes, the occurrence of
data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data …