This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …

Driver's driving style and driving skill have an essential influence on traffic safety, capacity,
and efficiency. Through clustering algorithms, extensive studies explore the risk assessment …

The electrification of the automotive has recently shaped the current revolution in
transportation. Many automotive companies are now producing their own electric vehicle …

Many approaches have been developed for dimensionality reduction. These approaches
can broadly be categorized into supervised and unsupervised methods. In case of …

In this article, an unsupervised feature selection algorithm is proposed using an improved
version of a recently developed Differential Evolution technique called MoDE. The proposed …

Features selected by a supervised/unsupervised technique often include redundant or
correlated features. While use of correlated features may result in an increase in the design …

Feature selection is selecting a subset of optimal features. Feature selection is being used in
high dimensional data reduction and it is being used in several applications like medical …