Feature Selection for mixed data is an active research area with many applications in
practical problems where numerical and non-numerical features describe the objects of …

Aiming at the problem of effectively selecting relevant features from heterogeneous data
without decision, a novel feature selection approach is studied based on fuzzy mutual …

Fuzzy rough set theory has been proved to be an effective tool to deal with uncertainty data.
Some different forms of fuzzy uncertainty measures have been introduced in fuzzy rough set …

Extraneous growth of scientific information over the Internet makes the searching task non-
trivial and as a consequence researchers are facing difficulties in finding relevant papers …

Companies have an increasing access to very large datasets within their domain. Analysing
these datasets often requires the application of feature selection techniques in order to …

Unsupervised feature selection is one of the important techniques for unsupervised
knowledge discovery, which aims to reduce the dimensionality of conditional feature sets as …

Abstract In recent years, Unsupervised Feature Selection (UFS) methods have attracted
considerable interest in different research areas due to their wide application in problems …

Background Traditional clustering techniques are typically restricted to either continuous or
categorical variables. However, most real-world clinical data are mixed type. This study aims …

Text clustering has been an overlooked field of text mining that requires more attention.
Several applications require automatic text organisation which relies on an information …

The study of learning behaviors with multi features is of great significance for interactive
cooperation. The data prediction and decision are to realize the comprehensive analysis …