Feature subset selection is the process of identifying and removing many irrelevant and
redundant features. Initially, the input micro array dataset is selected from the medical …

It is a well-known fact that a variety of cluster analysis techniques exist to group objects
which have characteristics related to one another. But the fact of the matter is the …

The differential feature recognition algorithm of breast cancer patients is presented in this
paper based on minimum spanning tree (MST) and F-statistics. The algorithm uses the …

Mutual information (MI) based feature selection methods are getting popular as its ability to
capture the nonlinear and linear relationship among random variables and thus it performs …

Many modern real world databases include redundant quantities of categorical data that
contribute in data processing and efficient decision-making with their advances in database …

The irrelevant features, along with redundant features, severely affect the accuracy of the
learning machines. Feature subset selection as the process of identifying and removing …

The success of metabolomics studies depends upon the" fitness" of each biological sample
used for analysis: it is critical that metabolite levels reported for a biological sample …

Data mining is the technique by which one can extract efficient and effective data from huge
amount of raw data. There are various techniques for extracting useful data. Attribute …