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Computer Science - Artificial Intelligence | Unsupervised Classification - Similarity Measures, Classical and Metaheuristic Approaches, and (Reviews)

Unsupervised Classification

Similarity Measures, Classical and Metaheuristic Approaches, and Applications

Bandyopadhyay, Sanghamitra, Saha, Sriparna

2013, XVIII, 262 p.

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From the reviews:

“The book focuses on emerging metaheuristic approaches to unsupervised classification, with an emphasis on a symmetry-based definition of similarity. … I found this book very appealing. I also thought of it as very valuable for my preoccupations towards the real-world application of unsupervised classification to medical imaging. I thus believe that, when reading this book, junior as well as experienced researchers will find many new challenging theoretical and practical ideas.” (Catalin Stoean, zbMATH, Vol. 1276, 2014)

“The book views clustering as a (multiobjective) optimization problem and tackles it with metaheuristics algorithms. More interestingly, the authors of this book propose the exploitation of the concepts of point and line symmetry to define new distances to be used in clustering techniques. … researchers in the field will surely appreciate it as a good reference on the use of the symmetry notion in clustering.” (Nicola Di Mauro, Computing Reviews, July, 2013)



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