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 …

Most of the common techniques in text mining are based on the statistical analysis of a term,
either word or phrase. Statistical analysis of a term frequency captures the importance of the …

An ensemble of clustering solutions or partitions may be generated for a number of reasons.
If the data set is very large, clustering may be done on tractable size disjoint subsets. The …

Purpose–The purpose of this paper is to provide a review of the issues related to cluster
analysis, one of the most important and primitive activities of human beings, and of the …

Deciphering useful information from electrophysiological data recorded from the brain, in-
vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and …

Network traffic monitoring is a very difficult task, given the amount of network traffic
generated even in small networks. One approach to facilitate this task is network traffic …

The significant properties of the maximum likelihood (ML) estimate are consistency,
normality, and efficiency. While it has been proven that these properties are valid when the …

During the span of a structure's service life, conditions such as wear, overload,
environmental degradation, and natural disasters may accelerate the degradation of the …

This paper proposes three novel parallel clustering algorithms based on the Kohonen's
SOM aiming at preserving the topology of the original dataset for a meaningful visualization …