Data clustering is the process of identifying natural groupings or clusters within
multidimensional data based on some similarity measure. Clustering is a fundamental …

Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such
that items within a cluster are more “similar” to each other than they are to items in the other …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

Pattern recognition is a fast growing area with applications in a widely diverse number of
fields such as communications engineering, bioinformatics, data mining, content-based …

Describing non-parametric and parametric theoretic classification and the training of
discriminant functions, this second edition includes new and expanded sections on neural …

Awareness of electric energy usage has both societal and economic benefits, which include
reduced energy bills and stress on non-renewable energy sources. In recent years, there …

This paper addresses three major issues associated with conventional partitional clustering,
namely, sensitivity to initialization, difficulty in determining the number of clusters, and …

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

Many validity measures have been proposed for evaluating clustering results. Most of these
popular validity measures do not work well for clusters with different densities and/or sizes …

In this paper, we present an agglomerative fuzzy k-means clustering algorithm for numerical
data, an extension to the standard fuzzy k-means algorithm by introducing a penalty term to …