The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

A comprehensive coverage of emerging and current technology dealing with heterogeneous
sources of information, including data, design hints, reinforcement signals from external …

Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

Having a good understanding of users' interests has become increasingly important for
online retailers hoping to create a personalized service for a target market. Generally …

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy
sets theory. In 1965, LA Zadeh had published “Fuzzy Sets”[335]. After only one year, the first …

Density based clustering techniques like DBSCAN are attractive because it can find arbitrary
shaped clusters along with noisy outliers. Its time requirement is O (n2) where n is the size of …

Recently several algorithms for clustering large data sets or streaming data sets have been
proposed. Most of them address the crisp case of clustering, which cannot be easily …

In this paper, classification of a large hydrochemical data set (more than 600 water samples
and 11 hydrochemical variables) from southeastern California by fuzzy c‐means (FCM) and …

The lower and upper approximations are basic concepts in rough fuzzy set theory. The
effective computation of approximations is very important for improving the performance of …