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

Today's data mostly does not include the knowledge of cluster number. Therefore, it is not
possible to use conventional clustering approaches to partition today's data, ie, it is …

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries
to reflect the profile of this area by focusing more on those subjects that have been given …

In cluster analysis, a fundamental problem is to determine the best estimate of the number of
clusters; this is known as the automatic clustering problem. Because of lack of prior domain …

Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

In this paper we present a novel grou** genetic algorithm for clustering problems. Though
there have been different approaches that have analyzed the performance of several …

In this article, a new symmetry based genetic clustering algorithm is proposed which
automatically evolves the number of clusters as well as the proper partitioning from a data …

Swarm intelligence has emerged as a worthwhile class of clustering methods due to its
convenient implementation, parallel capability, ability to avoid local minima, and other …

Clustering is a useful exploratory tool for gene-expression data. Although successful
applications of clustering techniques have been reported in the literature, there is no method …

In this work we consider spatial clustering problem with no a priori information. The number
of clusters is unknown, and clusters may have arbitrary shapes and density differences. The …