Search results for "cluster analysis."

Computational annotation of genes differentially expressed along olive fruit development

2009

Abstract Background Olea europaea L. is a traditional tree crop of the Mediterranean basin with a worldwide economical high impact. Differently from other fruit tree species, little is known about the physiological and molecular basis of the olive fruit development and a few sequences of genes and gene products are available for olive in public databases. This study deals with the identification of large sets of differentially expressed genes in developing olive fruits and the subsequent computational annotation by means of different software. Results mRNA from fruits of the cv. Leccino sampled at three different stages [i.e., initial fruit set (stage 1), completed pit hardening (stage 2) a…

Differential cycles of range contraction and expansion in European high mountain plants during the Late Quaternary: insights from Pritzelago alpina (…

2003

Nuclear DNA sequence variation of the internal transcribed spacer (ITS) and amplified fragment length polymorphisms (AFLPs) were used to illuminate the evolutionary history of Pritzelago alpina, a herbaceous perennial of (sub)alpine to nival habitats of the European high mountains. Maximum likelihood analysis of ITS sequences of P. alpina, Hornungia petraea and Hymenolobus procumbens (the 'Pritzelago alliance') resolved P. alpina and H. petraea as sister taxa. ITS divergence estimates support an origin for P. alpina in the Late Tertiary, while intraspecific diversification started in the Late Quaternary (0.4-0.9 million years ago). AFLP analysis of 76 individuals of P. alpina, representing …

The Proximity Tourism: Behaviours of Sicilian Tourists on Holiday in Cefalù

2007

Modeling crowd dynamics through coarse-grained data analysis

2018

International audience; Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and d…

Environmental Data Processing by Clustering Methods for Energy Forecast and Planning

2011

This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation…

Background subtraction and peak search from threefold gamma event data

1990

Abstract A method for subtracting background from triple-coincidence γ events is presented. In our data set it was used to remove 40% of the noise without affecting photopeaks with intensity of >18 counts. An example of performance of Ward's clustering algorithm applied to three-dimensional photopeak searching is also presented. Several standard clustering algorithms were found to be applicable only to background-subtracted data.

Polar Classification of Nominal Data

2013

Many modern systems record various types of parameter values. Numerical values are relatively convenient for data analysis tools because there are many methods to measure distances and similarities between them. The application of dimensionality reduction techniques for data sets with such values is also a well known practice. Nominal (i.e., categorical) values, on the other hand, encompass some problems for current methods. Most of all, there is no meaningful distance between possible nominal values, which are either equal or unequal to each other. Since many dimensionality reduction methods rely on preserving some form of similarity or distance measure, their application to such data sets…

Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis

2006

Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…

A completely automated CAD system for mass detection in a large mammographic database.

2006

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing secon…

Fuzzy technique for microcalcifications clustering in digital mammograms

2012

Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …