computational intelligence; fuzzy clustering; soft computing; fuzzifier; self-learning algorithms; possibilistic clustering; probabilistic clustering

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, sophisticated 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. In this book, we have developed methods for the adaptive possibilistic and probabilistic clustering with a variable fuzzifier by allocating blur parameters and suppressing fuzzy clustering results which allow assigning component separability and clusters’ compactness (in a sequential mode).