Cluster analysis means the organization of an unlabeled
collection of objects or patterns into separate groups based on
their similarity. The task of computerized data clustering has been
approached from diverse domains of knowledge like graph theory,
multivariate analysis, neural networks, fuzzy set theory, and so
on. Clustering is often described as an unsupervised learning
method but most of the traditional algorithms require a prior
specification of the number of clusters in the data for guiding the
partitioning process, thus making it not completely unsupervised.
Modern data mining tools that predict future trends and behaviors
for allowing businesses to make proactive and knowledge-driven
decisions, demand fast and fully automatic clustering of very large
datasets with minimal or no user intervention.
In this volume, we formulate clustering as an optimization
problem, where the best partitioning of a given dataset is achieved
by minimizing/maximizing one (single-objective clustering) or more
(multi-objective clustering) objective functions. Using several
real world applications, we illustrate the performance of several
metaheuristics, particularly the Differential Evolution algorithm
when applied to both single and multi-objective clustering
problems, where the number of clusters is not known beforehand and
must be determined on the run. This volume comprises of 7 chapters
including an introductory chapter giving the fundamental
definitions and the last Chapter provides some important research
challenges.
Academics, scientists as well as engineers engaged in research,
development and application of optimization techniques and data
mining will find the comprehensive coverage of this book
invaluable.
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