Search results for "clustering"

IMPLEMENTAZIONE DI UN ALGORITMO DI CLUSTERING PER L’IDENTIFICAZIONE DELLE PRECIPITAZIONI STRATIFORMI E CONVETTIVE ALLA SCALA D’EVENTO: UN’APPLICAZION…

A Projection Pursuit Algorithm for Preference Data

2018

In the framework of preference rankings, the interest can lie in finding which predictors and which interactions are able to explain the observed preference structures. The last years have seen a remarkable owering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, following the idea of Bolton (2003), a Projection Pursuit (PP) clustering algorithm for preference data will be proposed in order to extract useful inform…

Weakly controlled Moran constructions and iterated functions systems in metric spaces

2011

We study the Hausdorff measures of limit sets of weakly controlled Moran constructions in metric spaces. The separation of the construction pieces is closely related to the Hausdorff measure of the corresponding limit set. In particular, we investigate different separation conditions for semiconformal iterated function systems. Our work generalizes well known results on self-similar sets in metric spaces as well as results on controlled Moran constructions in Euclidean spaces.

Identification of patterns og change on mongitudinal data, illustrated by two exemples : study of hospital pathways in the management of cancer. Cons…

2014

Context In healthcare domain, data mining for knowledge discovery represent a growing issue. Questions about the organisation of healthcare system and the study of the relation between treatment and quality of life (QoL) perceived could be addressed that way. The evolution of technologies provides us with efficient data mining tools and statistical packages containing advanced methods available for non-experts. We illustrate this approach through two issues: 1 / What organisation of healthcare system for cancer diseases management? 2 / Exploring in patients suffering from metastatic cancer, the relationship between health-related QoL perceived and treatment received as part of a clinical tr…

An unsupervised quantitative method to analyse students' answering strategies to a questionnaire

2018

Questionnaires are perhaps the most widely used instruments to assess conceptual learning in physics as well as in mathematics. In the field of physics and mathematics education research it is surely interesting to be able to use a questionnaire as a “diagnostic instrument,” i.e., to know details about relationships among student answers to the different questions. In recent years several research works focused on this goal by using different quantitative methodologies, like Factor, Model and Cluster Analyses. However, very few research works deepened the theoretical aspects of the Cluster Analysis. In this contribution, we discuss two Cluster Analysis methods with respect to this issue. By…

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

2017

The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume…

Random forest analysis: a new approach for classication of Beta Thalassemia

2020

In recent years, Thalassemia care providers started classifying patients as transfusion- dependent-Thalassemia (TDT) or non-transfusion-dependent-Thalassemia (NTDT) owing to the established role of transfusion therapy in dening the clinical complication prole, although this classication was also based on expert opinion and is limited by reliance on patients'current transfusion status. Starting from a vast set of variables indicating severity phenotype, through the use of both classication and clustering techniques we want to explore the presence of two (TDT vs NTDT) or more clusters, in order to approaching to a new denition for the classication of Beta-Thalassemia in Thalassemia Syndromes …

Inferring networks from high-dimensional data with mixed variables

2014

We present two methodologies to deal with high-dimensional data with mixed variables, the strongly decomposable graphical model and the regression-type graphical model. The first model is used to infer conditional independence graphs. The latter model is applied to compute the relative importance or contribution of each predictor to the response variables. Recently, penalized likelihood approaches have also been proposed to estimate graph structures. In a simulation study, we compare the performance of the strongly decomposable graphical model and the graphical lasso in terms of graph recovering. Five different graph structures are used to simulate the data: the banded graph, the cluster gr…

Centroid-based Cluster Analysis of HVSR Data for Seismic Microzonation

2014

Horizontal to Vertical Spectral Ratio (HVSR) datasets acquired for studies of seismic microzoning in various urban centers of Sicilian towns, have been used to test clustering analysis through a nonhierarchical centroid-based algorithm. In this context clustering techniques may be useful to identify areas with similar seismic behaviour through HVSR data. Centroid-based algorithms generally require the number of clusters, k, and the initial centroid coordinates to be specified in advance. This aspect is considered to be one of the biggest drawbacks of these algorithms. The proposed algorithm doesn’t limit the number of k clusters and choose the initial centroids automatically from the data s…

QoS Based Routing Protocol for Intra-Mesh Infrastructure Communications

2015

International audience; Mesh Networks (WMNs) have been considered as a promising alternative to conventional wired networks, thanks to its flexibility and easy deployment. Thus, to ensure a satisfying level of QoS guarantees for real-time and streaming applications such as Voice over IP (VoIP) and Video on Demand (VoD), we propose a novel QoS based routing protocol for wireless mesh environments, called Hybrid QoS Mesh Routing (HQMR), jointly with a clustering algorithm to enhance scalability issues within the mesh infrastructure. HQMR is composed of two routing sub-protocols: a reactive routing sub-protocol for intra-infrastructure communications and a proactive QoS based multi-tree routin…