Search results for "Clustering"
showing 10 items of 446 documents
Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.
2017
Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study inclu…
Biallelic gephyrin variants lead to impaired GABAergic inhibition in a patient with developmental and epileptic encephalopathy
2021
Abstract Synaptic inhibition is essential for shaping the dynamics of neuronal networks, and aberrant inhibition is linked to epilepsy. Gephyrin (Geph) is the principal scaffolding protein at inhibitory synapses and is essential for postsynaptic clustering of glycine (GlyRs) and GABA type A receptors. Consequently, gephyrin is crucial for maintaining the relationship between excitation and inhibition in normal brain function and mutations in the gephyrin gene (GPHN) are associated with neurodevelopmental disorders and epilepsy. We identified bi-allelic variants in the GPHN gene, namely the missense mutation c.1264G > A and splice acceptor variant c.1315-2A > G, in a patient wi…
“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids
2017
A myriad of works has been published for achieving data clustering based on the Bayesian paradigm, where the clustering sometimes resorts to Naive-Bayes decisions. Within the domain of clustering, the Bayesian principle corresponds to assigning the unlabelled samples to the cluster whose mean (or centroid) is the closest. Recently, Oommen and his co-authors have proposed a novel, counter-intuitive and pioneering PR scheme that is radically opposed to the Bayesian principle. The rational for this paradigm, referred to as the “Anti-Bayesian” (AB) paradigm, involves classification based on the non-central quantiles of the distributions. The first-reported work to achieve clustering using the A…
Volatility Transmission Models: A Survey
2005
This study reviews the literature on volatility transmission in order to determine what we have learnt about the different methodologies applied. In particular, GARCH, regime switching and stochastic volatility models are analysed. In addition, this study covers several concrete aspects such as their scope of application, the overlapping problem, the concept of efficiency and asymmetry modelling. Finally, emerging topics and unanswered questions are identified, serving as an agenda for future research.
Time-Frequency Filtering for Seismic Waves Clustering
2014
This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.
A Geographical Self-Organizing Approach for Vehicular Networks
2012
Cooperative vehicular networks have always been considered as the perfect way to bring more comfort to the passengers and more safety to the human life. Thus, research community and governmental organizations are interested to study and deploy these networks. The vehicular networks principle is connecting vehicles to each other and to existing infrastructure. However, their industrialization faces some challenges: (i) high mobility, (ii) frequently partitioned network, (iii) geographically constrained topology, and (iv) scalability. Therefore, in contrast to traditional networks, vehicular network protocols focus on both achieving adequate QoS level and reducing overhead. Achieving these tw…
The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration
2014
Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …
Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators
2016
Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
Self-organizing maps could improve the classification of Spanish mutual funds
2006
In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from…