Fuzzy Neighborhood-Based Clustering
with Recent Theory and Applications
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When Prof. Zadeh introduced the concept of fuzzy sets that produced the idea of allowing to have membership functions to all clusters in 1965, his main objective was to set up a formal framework for the representation and management of vague and uncertain data. Today, besides the possibility to handle uncertainties within data, fuzzy data analysis allows us to learn knowledge-based representation of the information subsistent in the data. When writing this monograph, our intention was not only to give a self-contained and methodological introduction to fuzzy neighborhood-based cluster analysis...
When Prof. Zadeh introduced the concept of fuzzy sets that produced the idea of allowing to have membership functions to all clusters in 1965, his main objective was to set up a formal framework for the representation and management of vague and uncertain data. Today, besides the possibility to handle uncertainties within data, fuzzy data analysis allows us to learn knowledge-based representation of the information subsistent in the data. When writing this monograph, our intention was not only to give a self-contained and methodological introduction to fuzzy neighborhood-based cluster analysis with its areas of applications, but also to provide a systematic description of novel clustering methods. We think that the book will be useful for engineers, statisticians, and computer scientists in both teaching and research, who deal with data analysis, pattern recognition, bioinformatics, or who take into consideration the application of fuzzy neighborhood-based clustering methods in their area of work.
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