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 …

Now days, there are huge amount of data available for analysis, the main problem with the
data is inconsistency. The inconsistent data (missing value) need to replace with most …

Accepted: 15/Apr/2019, Published: 30/Apr/2019 Abstract-The problems of missing values in
the field of data mining have become emerging areas of research in recent years. It has …

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 …

Missing data imputation is an important step in the process of machine learning and data
mining when certain values are missed. Among extant imputation techniques, kNN …

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 is an important asset for any organization to successfully run its business. When we
collect data, it contains data with low qualities such as noise, incomplete, missing values etc …

Missing data is relatively common in all type of research, which can reduce the statistical
power and have biased results if not handled properly. Multivariate Imputation by Chained …

Data mining has made a great progress in recent year but the problem of missing data or
value has remained great challenge for data mining. Missing data or value in a datasets can …

Missing data is a common feature for large data sets in general. Imputation is a class of
procedures that aims to fill the missing values with estimated ones. This method involves …