a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Hu, Zhengbing creator author. Bodyanskiy, Yevgeniy V. author. Tyshchenko, Oleksii author. text bibliography enk 2019 2019 monographic eng 1 online resource (vii, 111 pages) ; cm. In today's data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications.In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning. Prelims -- Introduction -- Review of the problem area -- Adaptive methods of fuzzy clustering -- Kohonen maps and their ensembles for fuzzy clustering tasks -- Simulation results and solutions for practical tasks -- Conclusion -- References. Zhengbing Hu, Yevgeniy V. Bodyanskiy, and Oleksii K. Tyshchenko. Includes bibliographical references. Business Data processing Electronic data processing Fuzzy systems Business & Economics Research & Development Neural networks & fuzzy systems HF5548.2 .H89 2019 658 9781838671716 (e-book) https://www.emerald.com/insight/publication/doi/10.1108/9781838671716 https://www.emerald.com/insight/publication/doi/10.1108/9781838671716 UtOrBLW 190617 20251029120203.0 9781838671716 eng