Natural computing for unsupervised learning
著者
書誌事項
Natural computing for unsupervised learning
（Unsupervised and semi-supervised learning / M. Emre Celebi, series editor）
Springer, c2019
- : [hbk]
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and 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
目次
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.
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