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Metaheuristics for Data Clustering and Image Segmentation

  • Book
  • © 2019

Overview

  • Presents recent research on metaheuristics for data clustering and image segmentation
  • Includes a detailed study of the differential evolution and flower pollination algorithms
  • Written primarily for academic researchers who are interested in metaheuristics, data mining, computational intelligence, and intelligent data management

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 152)

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About this book

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.


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Table of contents (8 chapters)

Authors and Affiliations

  • Information Technology, University College of Bahrain, Manama, Bahrain

    Meera Ramadas

  • Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, USA

    Ajith Abraham

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