The evolution of big data analytics through machine learning and artificial intelligence
techniques has caused organizations in a wide range of sectors including health …

Many real-world applications encountered a common issue in data analysis is the presence
of missing data value and challenging task in many applications such as wireless sensor …

Missing data are the absence of data items for a subject; they hide some information that
may be important. In practice, missing data have been one major factor affecting data …

Missing data is common problem faced by researchers and data scientists. Therefore, it is
required to handle them appropriately in order to get better and accurate results of data …

Data mining requires a pre-processing task in which the data are prepared, cleaned,
integrated, transformed, reduced and discretized for ensuring the quality. Missing values is a …

Missing data is a major problem in terms of both machine learning and data mining
methods. Like most of these methods do not work with missing data, negative results may …

The concept of missing data is important to apply statistical methods on the dataset.
Statisticians and researchers may end up to an inaccurate illation about the data if the …

Big data has become a core technology to provide innovative solutions in many fields.
However, the collected dataset for data analysis in various domains will contain missing …

Missing values in datasets present an important problem for traditional and modern
statistical methods. Many statistical methods have been developed to analyze the complete …

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 …