The problem of missing values has been a prominent area of research in recent years. They
may prove to be a huge obstacle during the analysis of data related to various domains …

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 …

Clustering of incomplete data set containing missing values is a common problem in the
literature. Methods to handle this problem have vast variations, including several imputation …

Dealing with missing values has been a major obstacle in machine learning. The
occurrence of missing data is a significant problem that often results in a noticeable …

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem
in many research areas. The reasons for the lack of missing attributes may be several; …

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 pre-processing is one of the crucial phases of data mining that enhances the efficiency
of data mining techniques. One of the most important operations performed on data pre …

Data preprocessing and cleansing play a vital role in data mining by ensuring good quality
of data. Data-cleansing tasks include imputation of missing values, identification of outliers …

Various imputation approaches have been proposed to address the issue of missing values
in data mining and machine learning applications. To improve the accuracy of missing data …

Addressing missing values is a persistent challenge in the field of data mining. The
presence of incomplete data can significantly compromise the overall data quality …