Authors:
- Latest research on metaheuristic clustering
Part of the book series: Studies in Computational Intelligence (SCI, volume 178)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (7 chapters)
-
Front Matter
-
Back Matter
About this book
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.
Reviews
From the reviews:
“In this volume, the performance of DE is illustrated, 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. … The reader is carefully navigated through the efficacies of clustering, evolutionary optimization and a hybridization of the both.” (T. Postelnicu, Zentralblatt MATH, Vol. 1221, 2011)Authors and Affiliations
-
Jadavpur University, Calcutta, India
Swagatam Das, Amit Konar
-
Norwegian University of Science and Technology, Trondheim, Norway
Ajith Abraham
Bibliographic Information
Book Title: Metaheuristic Clustering
Authors: Swagatam Das, Ajith Abraham, Amit Konar
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-93964-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-92172-1Published: 24 March 2009
Softcover ISBN: 978-3-642-10071-0Published: 28 October 2010
eBook ISBN: 978-3-540-93964-1Published: 30 January 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XVIII, 252
Topics: Artificial Intelligence, Mathematical and Computational Engineering