Natural Computing for Unsupervised Learning

Includes advances on unsupervised learning using natural computing techniques

Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning

Features natural computing techniques such as evolutionary multi-objective algorithms, and many-objective swarm intelligence algorithms

Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms

Auteur

Xiangtao Li received the B.Eng. Degree, the M.Eng. and Ph.D. degrees in computer science from Northeast Normal University, Changchun, China in 2009, 2012, 2015, respectively. Now He is an associate professor in the Department of Computer science and information technology, Northeast Normal University. He has published more than 50 research papers. His research interests include intelligent computation, evolutionary data mining, constrained optimization, bioinformatics, computational biology and interdisciplinary research.
Ka-Chun Wong received the BEng degree in computer engineering from United College, Chinese University of Hong Kong, in 2008. He received the MPhil degree from the same university in 2010 and the PhD degree from the Department of Computer Science, University of Toronto in 2014. He assumed his duty as an assistant professor at City University of Hong Kong in 2015. His research interests include bioinformatics, computational biology, evolutionary computation, data mining, machine learning, and interdisciplinary research. He is merited as the associate editor of BioData Mining in 2016. In addition, he is on the editorial board of Applied Soft Computing since 2016. He has solely edited 2 books published by Springer and CRC Press, attracting 30 peer-reviewed book chapters around the world.

Contenu
Introduction.- Part I Basic Natural Computing Techniques for Unsupervised Learning.- Hard Clustering using Evolutionary Algorithms.- Soft Clustering using Evolutionary Algorithms.- Fuzzy / Rough Set Systems for Unsupervised Learning.- Unsupervised Feature Selection using Evolutionary Algorithms.- Unsupervised Feature Selection using Artificial Neural Networks.- Part II Advanced Natural Computing Techniques for Unsupervised Learning.- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering.- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection.- Co-Evolutionary Approaches for Unsupervised Learning.- Mining Evolving Patterns using Natural Computing Techniques.- Multi-objective Optimization for Unsupervised Learning.- Many-objective Optimization for Unsupervised Learning.- Part III Applications.- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques.- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data.- Natural Computing Techniques for Community Detection on Online Social Networks.- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning.- Conclusion. <p