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 …

Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

Previous research has resulted in a number of different algorithms for rule discovery. Two
approaches discussed here, the 'all-rules' algorithm and multi-objective metaheuristics, both …

Many metabolomics, and other high-content or high-throughput, experiments are set up
such that the primary aim is the discovery of biomarker metabolites that can discriminate …

The framework of multiobjective optimization is used to tackle the unsupervised learning
problem, data clustering, following a formulation first proposed in the statistics literature. The …

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 …

This paper is the second part of a two-part paper, which is a survey of multiobjective
evolutionary algorithms for data mining problems. In Part I, multiobjective evolutionary …

In the application of machine learning to real-life decision-making systems, eg, credit scoring
and criminal justice, the prediction outcomes might discriminate against people with …

Motivation: Biologists often employ clustering techniques in the explorative phase of
microarray data analysis to discover relevant biological grou**s. Given the availability of …

Metabolomics, like other omics methods, produces huge datasets of biological variables,
often accompanied by the necessary metadata. However, regardless of the form in which …