In this paper we perform an hierarchical clustering in high – dimensional spaces, without first applying any space reduction. Instead, in each step of the algorithm we perform a soft feature selection, witch does not have to be shared among all input elements. The main goal is to correctly identify the patterns that underly in the data. The proposed algorithm is applied, with promising results,...

The interest in reconstructing phylogenetic relationships from data on structural similarity of metabolic pathways is growing. The similarity notions and the techniques involved in this reconstruction are assessed by building phylogenetic relationships for model sets of organisms from the similarity measures of the same metabolic pathway for all of them, and then the phylogenetic trees obtained...

We apply the semi-analytical galaxy formation code of Cole et al. to investigate the formation of globular cluster (GC) systems in hierarchical clustering scenarios. The nature of the model allows us to investigate the properties of GC systems and their parent galaxies within a cosmological framework, over a wide dynamic range of mass and time resolution. Assuming GCs form during mergers of gas...

An algorithm is presented, which exhibits a computed number of rigid conformers of an input small molecule, covering the geometric diversity in the conformational space, with minimal structural redundancy. The algorithm calls a conformer generator, then performs an agglomerative hierarchical clustering with the modified clustering gain as the stop criterion. The number of classes is computed wi...

We have developed a simple colorimetric sensor array that detects a wide range of volatile analytes and then applied it to the detection of toxic gases. The sensor consists of a disposable array of cross-responsive nanoporous pigments with colours that are changed by diverse chemical interactions with analytes. Although no single chemically responsive pigment is specific for any one analyte, th...

We apply spectral biclustering to mortality datasets in order to capture three relevant aspects: the period, the age and the cohort effects, as their knowledge is a key factor in understanding actuarial liabilities of private life insurance companies, pension funds as well as national pension systems. While standard techniques generally fail to capture the cohort effect, on the contrary, biclus...

Quadratic bottom boundary layer drag is a potentially significant source of energy dissipation in the deep ocean. This study presents a new estimate of the energy dissipated by this process, using results from the world’s largest archive of ocean current meter time series and focusing strongly upon the potential uncertainties in this calculation. Two methods are used, one based on a simple divi...

Recent advances in wireless networking technologies introduce several energy aware routing protocols in sensor networks. Such protocols aim to extend the lifetime of network by reducing the energy consumption of nodes. Many researchers are looking for certain challenges that are predominant in the grounds of energy consumption. One such protocol that addresses this energy consumption issue is ‘...

This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clus...

This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process (HDP). Tree hierarchies are learned alon...