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

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

An image clustering method that is based on the particle swarm optimizer (PSO) is
developed in this paper. The algorithm finds the centroids of a user specified number of …

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is
proposed. This approach is applied to image segmentation. The proposed approach …

This chapter provides a comprehensive overview to the data clustering techniques, based
on naturally-inspired metaheuristic algorithms. At first the clustering problem, similarity and …

K-means clustering is widely used for exploratory data analysis. While its dependence on
initialisation is well-known, it is common practice to assume that the partition with lowest sum …

The role of grou** is to divide the set of objects into homogeneous groups: two arbitrary
objects belonging to the same group are more similar to each other than two arbitrary …

Abstract The performance of Spectral Clustering (SC) relies heavily on the choice of
similarity matrix used to compute pairwise similarities between data points, especially when …

Abstract International Revenue Sharing Fraud (IRSF) is the most persistent type of fraud in
the telco industry. Hackers try to gain access to an operator's network in order to make …

One of the numerous implementations of neural networks (NN), as part of machine learning,
is the modeling of network firewall rules. For this purpose, a suitable dataset containing …