Amit Konar Bücher

Cluster analysis organizes an unlabeled collection of objects into distinct groups based on similarity. This process has been explored through various domains, including graph theory, multivariate analysis, neural networks, and fuzzy set theory. While clustering is often labeled as an unsupervised learning method, many traditional algorithms require prior specification of the number of clusters, which limits its unsupervised nature. Modern data mining tools necessitate rapid and fully automatic clustering of large datasets with minimal user intervention to predict future trends and behaviors for informed decision-making. This volume presents clustering as an optimization problem, aiming to find the best partitioning of a dataset by minimizing or maximizing one or more objective functions. It showcases real-world applications and demonstrates the effectiveness of several metaheuristics, particularly the Differential Evolution algorithm, in both single and multi-objective clustering scenarios where the number of clusters is not predetermined. Comprising seven chapters, the book starts with fundamental definitions and concludes with significant research challenges. It offers valuable insights for academics, scientists, and engineers involved in optimization techniques and data mining research and applications.

The book delves into the intersection of cognitive sciences and cognitive systems engineering, addressing the rise of human-centric systems amidst the next information revolution. It emphasizes the importance of cognitive design and system engineering in the context of ubiquitous computing and Ambient Intelligence. By exploring computational models related to reasoning, learning, and multi-agent coordination, it provides insights into the psychological aspects of human cognition. The text covers diverse topics, including logic perspectives, distributed models, and intelligent robotics, making it a comprehensive resource.

Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of computational intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, criminal investigation, telecommunication networks, and intelligent robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own.

Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems.