𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Innovations in Fuzzy Clustering: Theory and Applications

✍ Scribed by Mika Sato-Ilic

Book ID
127449489
Publisher
Springer
Year
2006
Tongue
English
Weight
2 MB
Series
Studies in Fuzziness and Soft Computing
Edition
1
Category
Library
ISBN
3540343563

No coin nor oath required. For personal study only.

✦ Synopsis

There is a great interest in clustering techniques due to the vast amount of data generated in every field including business, health, science, engineering, aerospace, management and so on. It is essential to extract useful information from the data. Clustering techniques are widely used in pattern recognition and related applications.The research monograph presents the most recent advances in fuzzy clustering techniques and their applications. The following contents are included:- Introduction to Fuzzy Clustering- Fuzzy Clustering based Principal Component Analysis- Fuzzy Clustering based Regression Analysis- Kernel based Fuzzy Clustering- Evaluation of Fuzzy Clustering- Self-Organized Fuzzy ClusteringThis book is directed to the computer scientists, engineers, scientists, professors and students of engineering, science, computer science, business, management, avionics and related disciplines.

✦ Subjects

Математическая статистика

📜 SIMILAR VOLUMES

Advances in Fuzzy Clustering and its App
Advances in Fuzzy Clustering and its Applications
✍ Jose Valente de Oliveira, Witold Pedrycz 📂 Library 📅 2007 🏛 Wiley 🌐 English ⚖ 9 MB

A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book

Innovations in Machine Learning: Theory
Innovations in Machine Learning: Theory and Applications
✍ Holmes D. E., Jain L. C. (Ed) 📂 Library 📅 2006 🌐 English ⚖ 3 MB

"Machine learning is currently one of the most rapidly growing areas of research in computer science. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters i