Clustering is the unsupervised classification of patterns (observations, data items, or feature
vectors) into groups (clusters). The clustering problem has been addressed in many contexts …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

This paper describes a data organization system and genuine content-addressable memory
called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where …

Artificial Bee Colony (ABC) algorithm which is one of the most recently introduced
optimization algorithms, simulates the intelligent foraging behavior of a honey bee swarm …

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 …

Since the publishing of the first edition of Classification Methods for Remotely Sensed Data
in 2001, the field of pattern recognition has expanded in many new directions that make use …

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 …

Fisher-Rao linear discriminant analysis (LDA) is a valuable tool for multigroup classification.
LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for …

Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search
heuristics of current interest. This paper describes an application of DE to the automatic …

We propose a new learning method," Generalized Learning Vec (cid: 173) tor Quantization
(GLVQ)," in which reference vectors are updated based on the steepest descent method in …