With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …

Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Relevant feature identification has become an essential task to apply data mining algorithms
effectively in real-world scenarios. Therefore, many feature selection methods have been …

This paper introduces concepts and algorithms of feature selection, surveys existing feature
selection algorithms for classification and clustering, groups and compares different …

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 …

Feature selection is applied to reduce the number of features in many applications where
data has hundreds or thousands of features. Existing feature selection methods mainly focus …

Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

In this article, we describe an unsupervised feature selection algorithm suitable for data sets,
large in both dimension and size. The method is based on measuring similarity between …

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 …