Proximity curves for potential-based clustering

YesThe concept of proximity curve and a new algorithm are proposed for obtaining clusters in a finite set of data points in the finite dimensional Euclidean space. Each point is endowed with a potential constructed by means of a multi-dimensional Cauchy density, contributing to an overall anisotropic potential function. Guided by the steepest descent algorithm, the data points are successively visited and removed one by one, and at each stage the overall potential is updated and the magnitude of its local gradient is calculated. The result is a finite sequence of tuples, the proximity curve, whose pattern is analysed to give rise to a deterministic clustering. The finite set of all such proximity curves in conjunction with a simulation study of their distribution results in a probabilistic clustering represented by a distribution on the set of dendrograms. A two-dimensional synthetic data set is used to illustrate the proposed potential-based clustering idea. It is shown that the results achieved are plausible since both the ‘geographic distribution’ of data points as well as the ‘topographic features’ imposed by the potential function are well reflected in the suggested clustering. Experiments using the Iris data set are conducted for validation purposes on classification and clustering benchmark data. The results are consistent with the proposed theoretical framework and data properties, and open new approaches and applications to consider data processing from different perspectives and interpret data attributes contribution to patterns

Impact of environmental and genetic factors on the scale shape of zebrafish, Danio rerio (Hamilton 1822): A geometric morphometric study

Intraspecific morphological variability may reflect either genetic divergence among groups of individuals or response of individuals to environmental circumstances within the frame of phenotypic plasticity. Several studies were able to discriminate wild fish populations based on their scale shape. Here we examine whether the variations in the scale shape in fish populations could be related to genetic or environmental factors, or to both of them. In the first experiment, two inbred lines of zebrafish Danio rerio (Hamilton 1822) reared under identical environmental conditions were compared. Secondly, to find out what effect environmental factors might have, offsprings were divided into two groups and reared on different diets for 12 weeks. Potential recovery of scales from an environmental effect was also assessed. Experimental groups could successfully be distinguished according to the shape of scales in both experiments, and the results showed that both genetic and environmental factors may notably influence scale shape. It was concluded that scale shape analysis might be used as an explanatory tool to detect potential variability of environmental influences impacting genetically homogeneous groups of fish. However, due to its sensitivity to environmental heterogeneity, the applicability of this technique in identifying intraspecific stock membership of fish could be limited