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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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
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.
Authors and Affiliations
Bibliographic Information
Book Title: Metaheuristics for Data Clustering and Image Segmentation
Authors: Meera Ramadas, Ajith Abraham
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-030-04097-0
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-04096-3Published: 31 January 2019
eBook ISBN: 978-3-030-04097-0Published: 12 December 2018
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: IX, 163
Number of Illustrations: 19 b/w illustrations, 59 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Image Processing and Computer Vision, Algorithms