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Recent Advances in Hybrid Metaheuristics for Data Clustering (Hardcover)
Loot Price: R2,958
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Recent Advances in Hybrid Metaheuristics for Data Clustering (Hardcover)
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An authoritative guide to an in-depth analysis of various
state-of-the-art data clustering approaches using a range of
computational intelligence techniques Recent Advances in Hybrid
Metaheuristics for Data Clustering offers a guide to the
fundamentals of various metaheuristics and their application to
data clustering. Metaheuristics are designed to tackle complex
clustering problems where classical clustering algorithms have
failed to be either effective or efficient. The authors noted
experts on the topic provide a text that can aid in the design and
development of hybrid metaheuristics to be applied to data
clustering. The book includes performance analysis of the hybrid
metaheuristics in relationship to their conventional counterparts.
In addition to providing a review of data clustering, the authors
include in-depth analysis of different optimization algorithms. The
text offers a step-by-step guide in the build-up of hybrid
metaheuristics and to enhance comprehension. In addition, the book
contains a range of real-life case studies and their applications.
This important text: Includes performance analysis of the hybrid
metaheuristics as related to their conventional counterparts Offers
an in-depth analysis of a range of optimization algorithms
Highlights a review of data clustering Contains a detailed overview
of different standard metaheuristics in current use Presents a
step-by-step guide to the build-up of hybrid metaheuristics Offers
real-life case studies and applications Written for researchers,
students and academics in computer science, mathematics, and
engineering, Recent Advances in Hybrid Metaheuristics for Data
Clustering provides a text that explores the current data
clustering approaches using a range of computational intelligence
techniques.
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