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 Clustering
This book is directed to the computer scientists, engineers, scientists, professors and students of engineering, science, computer science, business, management, avionics and related disciplines.
Clustering has been around for many decades and located itself in a unique position as a fundamental conceptual and algorithmic landmark of data analysis. Almost since the very inception of fuzzy sets, the role and potential of these information granules in revealing and describing structure in data was fully acknowledged and appreciated. As a matter of fact, with the rapid growth of volumes of digital information, the role of clustering becomes even more visible and critical. Furthermore given the anticipated human centricity of the majority of artifacts of digital era and a continuous buildup of mountains of data, one becomes fully cognizant of the growing role and an enormous potential of fuzzy sets and granular computing in the design of intelligent systems. In the recent years clustering has undergone a substantial metamorphosis. From being an exclusively data driven pursuit, it has transformed itself into a vehicle whose data centricity has been substantially augmented by the incorporation of domain knowledge thus giving rise to the next generation of knowledge-oriented and collaborative clustering.