Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

Particle swarm optimization (PSO) is a popular heuristic method, which is capable of
effectively dealing with various optimization problems. A detailed overview of the original …

Data clustering is one of the most popular techniques in data mining. It is a process of
partitioning an unlabeled dataset into groups, where each group contains objects which are …

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 …

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

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to
improve the accuracy of traditional clustering approaches with applications in analyzing real …

Optimization based pattern discovery has emerged as an important field in knowledge
discovery and data mining (KDD), and has been used to enhance the efficiency and …

Data clustering is one of the most popular techniques in data mining. It is a method of
grou** data into clusters, in which each cluster must have data of great similarity and high …

Abstract Knowledge mining is an emerging field where various patterns, rules, etc. can be
generated which helps us in the analysis of the result. Medical information systems in …