We have witnessed great interest and a wealth of promise in content-based image retrieval
as an emerging technology. While the last decade laid foundation to such promise, it also …

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 …

The main objective of this paper is to further improve the current time-series prediction
(forecasting) algorithms based on hybrids between machine learning and nature-inspired …

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 …

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 …

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 …

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 …

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries
to reflect the profile of this area by focusing more on those subjects that have been given …

This book addresses the integration of two areas of computer science, namely data mining
and evolutionary algorithms. Both these areas have become increas ingly popular in the last …