Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

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

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 …

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to
spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to …

Clustering algorithms are a useful tool to explore data structures and have been employed
in many disciplines. The focus of this paper is the partitioning clustering problem with a …

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy
clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and …

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

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its
enhancements have been intensively explored, fuzzy clustering still suffers from the …

A comprehensive coverage of emerging and current technology dealing with heterogeneous
sources of information, including data, design hints, reinforcement signals from external …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …