Search results for "Clustering"

Using anatomic and metabolic imaging in stereotactic radio neuro-surgery treatments

2016

A physical-computational modelling for analysis of Centromere patterns in IIF images

2014

Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosing autoimmune diseases can be particularly difficult because these disorders can affect any organ or tissue in the body, and produce highly diverse clinical manifestations, depending on the site of autoimmune attack. Moreover, disease symptoms are often not apparent until the disease has reached a relatively advanced stage. These observations suggested us to develop an automated method to support the diagnosis. Developing an automated procedure for diagnosis of autoimmune disease…

A quantitative method to analyse an open-ended questionnaire: A case study about the Boltzmann Factor

2015

his paper describes a quantitative method to analyse an open- ended questionnaire. Student responses to a specially designed written questionnaire are quantitatively analysed by not hierarchical clustering called k-means method. Through this we can characterise behaviour students with respect their expertise to formulate explanations for phenomena or processes and/or use a given model in the different context. The physics topic is about the Boltzmann Factor, which allows the students to have a unifying view of different phenomena in different contexts

Euphosantianane E–G: Three New Premyrsinane Type Diterpenoids from Euphorbia sanctae-catharinae with Contribution to Chemotaxonomy

2019

Euphorbia species were widely used in traditional medicines for the treatment of several diseases. From the aerial parts of Egyptian endemic plant, Euphorbia sanctae-catharinae, three new premyrsinane diterpenoids, namely, euphosantianane E&ndash

Cluster analysis for portfolio optimization

2005

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio composition of the selected portfolio for a given value of the portfolio return are also investigated for each considered filtering method.

Kullback-Leibler distance as a measure of the information filtered from multivariate data

2007

We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation matrix from a reference model and we show that the expected values are known also when the specific model is unknown. We propose to make use of the Kullback-Leibler distance to estimate the information extracted from a correlation matrix by correlation filtering procedures. We also show how to use this distance to measure the stability of filtering procedures with respect to s…

Economic Sector Identification in a Set of Stocks Traded at the New York Stock Exchange: A Comparative Analysis

2006

We review some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a set of stocks traded at the New York Stock Exchange. The investigated time series are recorded at a daily time horizon. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methodologies provide different information about the considered set. Our comparative analysis suggests that th…

Effect of Topological Structure and Coupling Strength in Weighted Multiplex Networks

2018

Algebraic connectivity (second smallest eigenvalue of the supra-Laplacian matrix of the underlying multilayer network) and inter-layer coupling strength play an important role in the diffusion processes on the multiplex networks. In this work, we study the effect of inter-layer coupling strength, topological structure on algebraic connectivity in weighted multiplex networks. The results show a remarkable transition in the value of algebraic connectivity from classical cases where the inter-layer coupling strength is homogeneous. We investigate various topological structures in multiplex networks using configuration model, the Barabasi-Albert model (BA) and empirical data-set of multiplex ne…

Minimum Description Length Based Hidden Markov Model Clustering for Life Sequence Analysis

2010

In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures. nonPeerReviewed

A Clustering Approach to texture Classification

1988

In the paper a clustering technique to segment an image in to “homogeneous” regions is studied. The homogeneity of each region is evaluated by means of a “proximity function” computed between the pixels. The main result of such approach is that no-histogramming is required in order to perform segmentation. Possibilistic and probabilistic approaches are, also, combined to evaluate the significativity of the computed regions.