Search results for "Cluster Analysis"
showing 10 items of 848 documents
Segmentación del turista deportivo: el caso del espectador de la Fórmula 1
2021
El trabajo pretende identificar grupos de turistas de un evento deportivo, el GP Europa de Fórmula 1, con relación a su motivación para el consumo deportivo y características básicas que definen dichos grupos para conocer los perfiles de turista de un gran evento deportivo. Para caracterizar a los sujetos se utilizó el análisis clúster con una muestra de 148 asistentes al evento, turistas nacionales e internacionales. Los resultados obtenidos muestran la existencia de cuatro grupos de sujetos bien definidos: los sociales (formado por los que obtienen fuertes connotaciones sociales asistiendo al evento, el 17,6% de la muestra), los ostentosos (son el 17,5% de la muestra, que sin una motivaci…
Large scale semi-supervised image segmentation with active queries
2011
A semiautomatic procedure to generate classification maps of remote sensing images is proposed. Starting from a hierarchical unsupervised classification, the algorithm exploits the few available labeled pixels to assign each cluster to the most probable class. For a given amount of labeled pixels, the algorithm returns a classified segmentation map, along with confidence levels of class membership for each pixel. Active learning methods are used to select the most informative samples to increase confidence in the class membership. Experiments on a AVIRIS hyperspectral image confirm the effectiveness of the method, especially when used with active learning query functions and spatial regular…
A New Platform for Automatic Bottom-Up Electric Load Aggregation
2017
In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and …
On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists
2020
With the advent of “Big Data” as a field, in and of itself, there are at least three fundamentally new questions that have emerged, namely the Artificially Intelligence (AI)-based algorithms required, the hardware to process the data, and the methods to store and access the data efficiently. This paper (The work of the second author was partially supported by NSERC, the Natural Sciences and Engineering Council of Canada. We are very grateful for the feedback from the anonymous Referees of the original submission. Their input significantly improved the quality of this final version.) presents some novel schemes for the last of the three areas. There have been thousands of papers written rega…
A PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hour…
2021
AbstractUnderstanding the structure of precipitation and its separation into stratiform and convective components is still today one of the important and interesting challenges for the scientific community. Despite this interest and the advances made in this field, the classification of rainfall into convective and stratiform components is still today not trivial. This study applies a novel criterion based on a clustering approach to analyze a high temporal resolution precipitation dataset collected for the period 2002–2018 over the Sicily (Italy). Starting from the rainfall events obtained from this dataset, the developed methodology makes it possible to classify the rainfall events into f…
Classification of cat ganglion retinal cells and implications for shape-function relationship
2002
This article presents a quantitative approach to ganglion cell classification by considering combinations of several geometrical features including fractal dimension, symmetry, diameter, eccentricity and convex hull. Special attention is given to moment and symmetry-based features. Several combinations of such features are fed to two clustering methods (Ward's hierarchical scheme and K-Means) and the respectively obtained classifications are compared. The results indicate the superiority of some features, also suggesting possible biological implications.
Shape-Based Features for Cat Ganglion Retinal Cells Classification
2002
This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward’s hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.
Mammographic images segmentation based on chaotic map clustering algorithm
2013
Background: This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods: The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads…
Unsupervised change detection with kernels
2012
In this paper an unsupervised approach to change detection relying on kernels is introduced. Kernel based clustering is used to partition a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained the estimated representatives (centroids) of each group are used to assign the class membership to all others pixels composing the multitemporal scenes. Different approaches of considering the multitemporal information are considered with accent on the computation of the difference image directly in the feature spaces. For this purpose a difference kernel approach is successfully adopted. Finally an effective way to cope with the estimation o…
A systematic comparison of kinetic modelling methods generating parametric maps for [11C]-(R)-PK11195
2006
[(11)C]-(R)-PK11195 is presently the most widely used radiotracer for the monitoring of microglia activity in the central nervous system (CNS). Microglia, the resident immune cells of the brain, play a critical role in acute and chronic diseases of the central nervous system and in host defence against neoplasia. The purpose of this investigation was to evaluate the reliability and sensitivity of five kinetic modelling methods for the formation of parametric maps from dynamic [(11)C]-(R)-PK11195 studies. The methods we tested were the simplified reference tissue model (SRTM), basis pursuit, a simple target-to-reference ratio, the Logan plot and a wavelet based Logan plot. For the reliabilit…