Missing data occurs when variables or observations are missing. Researchers exclude or
impute influenced variables and data. This study proposes Fuzzy K-Top Matching Value …

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 …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

Missing value is a problem that is still often found in many studies. Missing value is where
data or data features are not available completely and intact. This still happens a lot in …

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 …

In the era of big data, a significant amount of data is produced in many applications areas.
However due to various reasons including sensor failures, communication failures …

Data cleaning is one step in the preprocessing which in the process often found missing
values in the dataset. Missing values is the condition of the absence of data items on a …

High quality data is essential in every field of research for valid research findings. The
presence of missing data in a dataset is common and occurs for a variety of reasons such as …

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

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of
missing values could lead to a wrong prediction. In this era of big data, when a massive …