Search results for "cluster analysis"

Motion sensors for activity recognition in an ambient-intelligence scenario

2013

In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is…

Il filo e il labirinto: l'analisi quantitativa

2006

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 …

Mosaic floors of roman Villa del Casale: Principal component analysis on spectrophotometric and colorimetric data

2013

Abstract Spectrophotometric and colorimetric data obtained during a measurement campaign aimed at supporting the Roman "Villa del Casale" (Piazza Armerina, Sicily, Italy) conservation activities, are presented. Special attention was paid to the possible variation of the chromatic coordinates, possibly due to the interventions of cleaning, consolidation, and protection. Data have been analyzed by the Principal Component Analysis (PCA) statistical technique, with the attempt to investigate its role in data variability reduction and verify its effectiveness in interpreting the phenomena occurring on the mosaic surface of the Villa, through grouping the observations into homogenous clusters. Ef…

Cap Rate as the Interpretative Variable of the Urban Real Estate Capital Asset: A Comparison of Different Sub‐Market Definitions in Palermo, Italy

2017

Real estate capital is in constant competition with other capital assets due to its different and complementary economic functions such as direct use, productive investment, and speculative investment. These features and the resulting opportunities cannot be easily deduced from direct observation of the real estate markets, so some further insights need to be carried out in order to highlight the relationship between prices, rents and performances. This study aims at providing a multifaceted perspective of a specific urban real estate market to overcome the difficulties arising from opacities and informative asymmetries that hinder the decision of investors, by facilitating the comparison o…

Oxidative stress response of tumor cells: microarray-based comparison between artemisinins and anthracyclines

2004

The antimalarial artemisinins also reveal profound cytotoxic activity against tumor cells. Artemisinins harbor an endoperoxide bridge whose cleavage results in the generation of reactive oxygen species (ROS) and/or artemisinin carbon-centered free radicals. Established cancer drugs such as anthracyclines also form ROS and free radicals that are responsible for the cardiotoxicity of anthracyclines. In contrast, artemisinins do not reveal cardiotoxicity. In the present investigation, we compared the cytotoxic activities of different artemisinins (artemisinin, artesunate, arteether, artemether, artemisitene, dihydroartemisinylester stereoisomers) in 60 cell lines of the National Cancer Institu…

Energy-efficient routing control algorithm in large-scale WSN for water environment monitoring with application to Three Gorges Reservoir area

2013

Published version of an article in the journal: The Scientific World Journal. Also available from the publisher at: http://dx.doi.org/10.1155/2014/802915 Open Access The typical application backgrounds of large-scale WSN (wireless sensor networks) for the water environment monitoring in the Three Gorges Reservoir are large coverage area and wide distribution. To maximally prolong lifetime of large-scale WSN, a new energy-saving routing algorithm has been proposed, using the method of maximum energy-welfare optimization clustering. Firstly, temporary clusters are formed based on two main parameters, the remaining energy of nodes and the distance between a node and the base station. Secondly,…

Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine

2013

Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…

A Cluster Analysis of Stock Market Data Using Hierarchical SOMs

2016

The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able…

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 …