Search results for " data"

Ohjelmiston kehittäminen puutteellisen ja virheellisen havaintoaineiston käsittelyyn

2002

Intrusion detection applications using knowledge discovery and data mining

2014

Determinants of current account balances

2011

Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa

2007

International audience; The response of photosynthetic activity to interannual rainfall variations in Africa South of the Sahara is examined using 20 years (1981-2000) of Normalised Difference Vegetation Index (NDVI) AVHRR data. Linear correlations and regressions were computed between annual NDVI and annual rainfall at a 0.5° latitude/longitude resolution, based on two gridded precipitation datasets (Climate Prediction Center Merged Analysis of Precipitation [CMAP] and Climatic Research Unit [CRU]). The spatial patterns were then examined to detect how they relate to the mean annual rainfall amounts, land-cover types as from the Global Land Cover 2000 data set, soil properties and soil typ…

USING HIGH RESOLUTION RAINGAUGE DATA FOR STORM TRACKING ANALYSIS IN THE URBAN AREA OF PALERMO, ITALY

2009

This paper presents a comparative analysis between rain-gauge storm tracking techniques in order to achieve a better knowledge of the rainfall dynamics over an urbanized area. The temporal and spatial distribution and kinematics of short term rainfall are recognized as one of the most important reasons in error production in rainfall-runoff on urban catchments. The uncertainty due to rainfall variability can greatly affect urban drainage modeling performance and reliability thus reducing the confidence of operators in their results. Modeling representations of urban catchments and drainage systems are commonly adopted for surface flooding forecasting and management and an adequate knowledge…

Improvements and applications of the elements of prototype-based clustering

2018

Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outl…

Rationale and design of the DARWIN-T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes): A multicenter retrospective nationwide Italian study a…

2017

Background Randomized controlled trials (RCTs) in the field of diabetes have limitations inherent to the fact that design, setting, and patient characteristics may be poorly transferrable to clinical practice. Thus, evidence from studies using routinely accumulated clinical data are increasingly valued. Aims We herein describe rationale and design of the DARWIN-T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes), a multicenter retrospective nationwide study conducted at 50 specialist outpatient clinics in Italy and promoted by the Italian Diabetes Society. Data synthesis The primary objective of the study is to describe the baseline clinical characteristics (particularly HbA1c) of pa…

ENSEMBLE METHODS FOR RANKING DATA

2017

The last years have seen a remarkable flowering of works about the use of decision trees for ranking data. 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, as ensemble methods, in order to find which predictors are able to explain the preference structure. In this work ensemble methods as BAGGING and Random Forest are proposed, from both a theoretical and computational point of view, for deriving classification trees when ranking data are observed. The advantages of these procedures are shown through an example on the SUSHI data set.

Ensemble methods for ranking data with and without position weights

2020

The main goal of this Thesis is to build suitable Ensemble Methods for ranking data with weights assigned to the items’positions, in the cases of rankings with and without ties. The Thesis begins with the definition of a new rank correlation coefficient, able to take into account the importance of items’position. Inspired by the rank correlation coefficient, τ x , proposed by Emond and Mason (2002) for unweighted rankings and the weighted Kemeny distance proposed by García-Lapresta and Pérez-Román (2010), this work proposes τ x w , a new rank correlation coefficient corresponding to the weighted Kemeny distance. The new coefficient is analized analitically and empirically and represents the main…

Repeated kidney re-transplantation—the Eurotransplant experience: a retrospective multicenter outcome analysis

2020

Transplant international (2020). doi:10.1111/tri.13569