In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Over the last five years there has been an increase in the frequency and diversity of network
attacks. This holds true, as more and more organizations admit compromises on a daily …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

The recent advancements in science, engineering, and technology have facilitated huge
generation of datasets. These huge datasets contain noisy, redundant, and irrelevant …

Feature selection has aroused considerable research interests during the last few decades.
Traditional learning-based feature selection methods separate embedding learning and …

In many data analysis tasks, one is often confronted with very high dimensional data.
Feature selection techniques are designed to find the relevant feature subset of the original …

In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in helping reduce high-dimensionality in machine learning …

Dimensionality reduction techniques can be categorized mainly into feature extraction and
feature selection. In the feature extraction approach, features are projected into a new space …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Clustering is a common unsupervised learning technique used to discover group structure in
a set of data. While there exist many algorithms for clustering, the important issue of feature …