Search results for " Clustering"
showing 10 items of 312 documents
Towards a new mix design method for asphalt mixtures containing rap
2016
Recycling is one of the most important aspects to conceive the new construction or rehabilitation of sustainable road infrastructures. Even if Reclaimed Asphalt Pavement (RAP) is commonly used in the practice, its presence may lead to some critical aspects due to the physical and chemical phenomena occurring during the new mix process. For instance, the formation of RAP clusters during a new mix may inhibit the uniform distribution of the virgin binder as well as cause changes in the design grading curve of the mixture. Indeed, laboratory results demonstrate that small-size RAP particles stick together forming clusters. The amount of clusters depends on different parameters observed: The pe…
K-means Clustering to Study How Student Reasoning Lines Can Be Modified by a Learning Activity Based on Feynman’s Unifying Approach
2017
Background:Research in Science Education has shown that often students need to learn how to identify differences and similarities between descriptive and explicative models. The development and use of explicative skills in the field of thermal science has always been a difficult objective to reach. A way to develop analogical reasoning is to use in Science Education unifying conceptual frameworks.Material and methods:A questionnaire containing six open-ended questions on thermally activated phenomena was administered to the students before instruction. A second one, similar but focused on different physical content was administered after instruction. Responses were analysed using k-means Cl…
Impact of the COVID-19 pandemic on music: a method for clustering sentiments
2021
The outbreak of coronavirus disease 2019 (COVID-19) was highly stressful for people. In general, fear and anxiety about a disease can be overwhelming and cause strong emotions in adults and children. One way to cope with this stress consists in listening to music. Aim of this work is to understand if the music heard during the lock-down reflects the emotions generated by the pandemic on each of us. So, the primary goal of this work is to build two indices for measuring the anger and joy levels of the top streamed songs by Italian Spotify users (during the SARS-CoV-2 pandemic), and study their evolution over time. A Hierarchical Cluster Analysis has been applied in order to identify groups o…
Mitigating DDoS using weight‐based geographical clustering
2020
Distributed denial of service (DDoS) attacks have for the last two decades been among the greatest threats facing the internet infrastructure. Mitigating DDoS attacks is a particularly challenging task as an attacker tries to conceal a huge amount of traffic inside a legitimate traffic flow. This article proposes to use data mining approaches to find unique hidden data structures which are able to characterize the normal traffic flow. This will serve as a mean for filtering illegitimate traffic under DDoS attacks. In this endeavor, we devise three algorithms built on previously uncharted areas within mitigation techniques where clustering techniques are used to create geographical clusters …
Regional inequality and economic development in Spain, 1860–2010
2016
Abstract Fifty years ago Jeffrey G. Williamson suggested that during the process of economic development regional income differences trace out an inverted U-shaped pattern. Since then several studies have tested this hypothesis. Yet, most of these only explore particular stages of development. This study, however, investigates the long-term evolution of regional income inequality. Using a novel dataset spanning 150 years, we describe per-capita GDP disparities across Spanish provinces (NUTS3) from 1860 to 2010. Moreover, to gain a deeper understanding of regional inequality, we examine other relevant dimensions: modality, mobility and spatial clustering. Overall, the findings confirm the ex…
Clustering Quality and Topology Preservation in Fast Learning SOMs
2008
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original …
Searches for clustering in the time integrated skymap of the ANTARES neutrino telescope
2014
Adrián-Martínez, S. et al.
Thinking outside the box: effects of modes larger than the survey on matter power spectrum covariance
2012
Considering the matter power spectrum covariance matrix, it has recently been found that there is a potentially dominant effect on mildly non-linear scales due to power in modes of size equal to and larger than the survey volume. This {\it beat coupling} effect has been derived analytically in perturbation theory and while it has been tested with simulations, some questions remain unanswered. Moreover, there is an additional effect of these large modes, which has so far not been included in analytic studies, namely the effect on the estimated {\it average} density which enters the power spectrum estimate. In this article, we work out analytic, perturbation theory based expressions including…
An original way to evaluate daily rainfall variability simulated by a regional climate model: the case of South African austral summer rainfall
2014
We discuss the value of a clustering approach as a tool for evaluating daily rainfall output from climate models. Ascendant hierarchical clustering is used to evaluate how well South African recurrent daily rainfall patterns are simulated during the austral summer (December to February 1970–1971 to 1998–1999). A set of 35-km regional climate simulations, run with the WRF model and driven by the ERA40 reanalysis, is chosen as a case study. Six recurrent patterns are identified and compared to the observed clusters obtained by applying the same methodology to 5352 daily rain gauge records. Two of the WRF clusters describe either a persistent and widespread dryness (65% of the days) or pattern…
An Analysis of Regional and Intra-annual Precipitation Variability over Iran using Multivariate Statistical Methods
1998
The temporal and spatial precipitation regime of Iran was analysed using multivariate analyses of monthly mean precipitation records for 71 stations. A Principal Component Analysis was applied to the correlation matrix in order to describe the intra-annual variations of precipitation. The Principal Component scores were mapped to visualize the spatial structure of the three derived precipitation regimes. By applying an agglomerative clustering (WARD) of the three Principal Component scores, five homogeneous spatial clusters, representing five precipitation regions, were developed. The intra-annual types of precipitation distribution, shown by the five clusters, are described and discussed.