Search results for "CLUSTER"
showing 10 items of 3640 documents
El profesorado de la Universidad de La Rioja: ¿resistente al burnout?
2019
El cansancio emocional, la despersonalización y la falta de realización personal son los tres rasgos característicos del burnout o Síndrome de Quemarse por el Trabajo (SQT), un síndrome conceptualizado como una respuesta emocional ante el estrés laboral crónico. El objetivo principal de esta investigación fue conocer la prevalencia del SQT en la Universidad de La Rioja. Se realizó un muestreo estratificado con afijación proporcional a los departamentos de esta universidad y 138 docentes respondieron al Maslach Burnout Inventory. Los resultados indicaron que un 9,4% de los docentes presenta niveles medios o altos de SQT, una cifra que indica, en congruencia con estudios de la misma naturalez…
Regional Disparities and Spatial Dependence of Bankruptcy in Spain
2021
Firm survival, bankruptcy, and turnaround are of great interest nowadays. Bankruptcy is the ultimate resource for a company to survive when it is affected by a severe decline. Thus, determinants of firm turnaround and survival in the context of bankruptcy are of interest to researchers, managers, and policy-makers. Prior turnaround literature has broadly studied firm-specific factors for turnaround success. However, location-specific factors remain relatively unstudied despite their increasing relevance. Thus, this paper aims to evaluate the existence of spatial dependence on the outcome of the bankruptcy procedure. Economic geography and business literature suggest that location matters an…
Intrusion detection applications using knowledge discovery and data mining
2014
Buone pratiche e strumenti di analisi per l’apprendimento, l’insegnamento e l’inclusione
2022
Il presente lavoro, nato in seno al progetto di ricerca “Best practices and tools of analysis in schools and community contexts: learning, teaching & inclusion”, avviato su fondi del Dipartimento SPPEFF dell’Università di Palermo, nel marzo 2019 e giunto alla sua seconda fase. La metodologia di ricerca utilizzata – perché ritenuta adeguata a perseguire le finalità fissate e a fornire una visione complessa ed articolata del fenomeno investigato – è stata quella del Mixed Method, con particolare riferimento all’Explanatory Design: Participant Selection Model di Creswell, Plano Clark, et al. (2003). L’«Explanatory Design is a two-phase mixed methods design. The overall purpose of this desi…
Exploring a large dataset : typical behavior of UHF signal propagation
2020
Radioverkon suunnittelua ja käyttöä varten täytyy radio aaltojen eteneminen ymmärtää hyvin. Tässä tutkimuksessa tutustutaan laajaan mittausaineistoon hetkellisiä tehoja maanlaajuisesta UHF verkosta. Spektrianalyysillä todettiin mitatussa tehossa olevan jaksollista vaihtelua taajuuksilla kerran ja kahdesti päivässä. Myös nopeampaa vaihtelua välillä 0:1 mHz ja 1:4 mHz todettiin 34% yhteyksistä. Hierarkisella ryhmittelyllä etsittiin tyypilliset mittausten arvojakaumat. Saaduissa arvojakaumien ryhmissä oli eri levyisiä vasemmalle tai oikealle vinoja tai symmetrisiä jakaumia. The design and operation of radio networks requires good understanding of radio propagation. This study explores a datase…
Improving Scalable K-Means++
2021
Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation …
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…
Proton reduction by phosphinidene-capped triiron clusters
2021
Bis(phosphinidene)-capped triiron carbonyl clusters, including electron rich derivatives formed by substitution with chelating diphosphines, have been prepared and examined as proton reduction catalysts. Treatment of the known cluster [Fe3(CO)9(µ3-PPh)2] (1) with various diphosphines in refluxing THF (for 5, refluxing toluene) afforded the new clusters [Fe3(CO)7(µ3-PPh)2(κ2-dppb)] (2), [Fe3(CO)7(µ3-PPh)2(κ2-dppv)] (3), [Fe3(CO)7(µ3-PPh)2(κ2-dppe)] (4) and [Fe3(CO)7(µ3-PPh)2(µ-κ2-dppf)] (5) in moderate yields, together with small amounts of the corresponding [Fe3(CO)8(µ3-PPh)2(κ1-Ph2PxPPh2)] cluster (x = -C4H6-, -C2H2-, -C2H4-, -C3H6-, -C5H4FeC5H4-). The molecular structures of complexes 3 a…
The SO2F2 quasi-spherical top: Correspondence between tensorial and Watson's formalisms
2006
Abstract The SO2F2 quasi-spherical top molecule with C2v symmetry is considered as a distorted spherical top deriving from the SO 4 2 − tetrahedral ion. We present here a detailed correspondence between the tensorial formalism using the Td⊃C2v reorientation and the usual Hamiltonian of Watson. We have also performed ab initio calculations in order to determine the centrifugal distorsion constants in the vibrational ground state.
Kernel Feature Extraction Methods for Remote Sensing Data Analysis
2014
Technological advances in the last decades have improved our capabilities of collecting and storing high data volumes. However, this makes that in some fields, such as remote sensing several problems are generated in the data processing due to the peculiar characteristics of their data. High data volume, high dimensionality, heterogeneity and their nonlinearity, make that the analysis and extraction of relevant information from these images could be a bottleneck for many real applications. The research applying image processing and machine learning techniques along with feature extraction, allows the reduction of the data dimensionality while keeps the maximum information. Therefore, develo…