Knowledge-Based Clustering: From Data to Information Granules - Tapa dura

Acerca del autor

WITOLD PEDRYCZ, PHD, is a Professor and Canada Research Chair at the University of Alberta, Canada. He is also with the Systems Research Institute of The Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a Fellow of the IEEE, has authored nine research monographs, edited six volumes, and has written numerous papers in computational intelligence, granular computing, pattern recognition, quantitative software engineering, and data mining.

De la contraportada

Discover the latest powerful tools in knowledge management

Knowledge-Based Clustering demonstrates how to design navigational platforms that enable information seekers to make sense of and better exploit highly diverse and heterogeneous sets of data. Moving beyond fuzzy clustering, the author shows how the promising new paradigm of knowledge-based clustering can reveal more meaningful data structure and enable society to better cope with the ever-growing flood of data and information. With this book, readers come to understand the fundamentals of knowledge-based clustering and its associated algorithms, and then learn to apply their knowledge to system modeling and design.

The book begins with an introduction to the field and a discussion of fuzzy clustering and granular computing. Then, the author delves into logic-based neurons and ensuing neural networks. The core part of the book consists of nine chapters in which highly diversified methodologies of knowledge-based clustering are presented and analyzed. The third section of the book is devoted to models, beginning with a discussion of the hyperbox architectures and then moving on to granular mappings and linguistic models.

All the tools and guidance needed to understand and master this exciting new field are provided:

• Numerous practical examples illustrating key concepts
• Reproducible experiments that offer readers the opportunity for hands-on experience
• Comprehensive coverage of prerequisites that set the foundation for complex algorithms and modeling
• Conclusion section at the end of each chapter that emphasizes the key points needed to move forward in the text
• References plus an extensive bibliography leading to further avenues of exploration on specialized topics

This is must reading for researchers, professionals, and students interested in clustering, fuzzy clustering, unsupervised learning, neural networks, fuzzy sets, pattern recognition, and system modeling. With the author's emphasis on mastering the prerequisites, coupled with carefully constructed practical examples and experiments, readers will be well on their way to becoming knowledge-based clustering experts themselves.