Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

Clustering algorithms aim at modeling fuzzy (ie, ambiguous) unlabeled patterns efficiently.
Our goal is to propose a theoretical framework where the expressive power of clustering …

The clustering problem is cast in the framework of possibility theory. The approach differs
from the existing clustering methods in that the resulting partition of the data can be …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

In recent years, a great number of publications have explored the use of genetic algorithms
as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this …

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule
generation algorithms. Rule generation from artificial neural networks is gaining in …

Foresight can be crucial in process and production control, production-and-resources
planning and in management decision making generally. Although forecasting the future …

Dataclusteringisacommontechniqueforstatis…, whichisusedin many? elds, including
machine learning, data mining, pattern recognition, image analysis and bioinformatics …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …