1,721,020 research outputs found
L'oxycéphalie : un cas chez Un Nord-Africain
No full textVassal Pierre A., Bellalouna (A.), Massari (R.). L'oxycéphalie : un cas chez Un Nord-Africain. In: Bulletins et Mémoires de la Société d'anthropologie de Paris, X° Série. Tome 6 fascicule 4-5, 1955. pp. 181-198
Fuzzy clustering of mixed data
No full textA fuzzy clustering model for data with mixed features is proposed. The clustering model allows different types of variables, or attributes, to be taken into account. This result is achieved by combining the dissimilarity measures for each attribute by means of a weighting scheme, so as to obtain a distance measure for multiple attributes. The weights are objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. Two simulation studies and two empirical applications were carried out that show the effectiveness of the proposed clustering algorithm in finding clusters that would be otherwise hidden if a multi–attributes approach were not pursued
Mapping susceptibility to landsliding in the central Apennines, Italy
No full textGeneralised linear modelling was used to model the relation between landsliding and several independent variables (geology, dip, strike, strata-slope interaction, aspect, density of lineaments and slope angle) for a small area of the central Apennines, Italy. Raster maps of landsliding and the independent variables were produced from air photographs, topographic and geological maps, and field checking. A logistic regression was then obtained between all slope movements and the independent variables (chosen to reflect conditions prior to landsliding). Not surprisingly, geology and slope angle were found to be the most significant factors in the model. The landslides in the region were then classified into dormant and active types and further linear models were obtained for each. While geology and slope angle were again the most significant factors in each model, slope aspect and strike were less significant for active landslides. Finally, further independent variables applicable to active landslides only (vegetation cover, soil thickness, horizontal curvature, vertical curvature, concavity of slope, local relief and roughness) were added to the model for active landslides. Interestingly, with these new variables added, vegetation cover and concavity of slope were found to be more significant than geology and slope angle. <br/
Satisfaction and Tourism Expenditure Behaviour
Full text linkIn the literature, the quantification of the effect of satisfaction on tourists’ expenditure behaviour has not been extensively studied. This research aims to fill in this gap, providing additional information about this crucial relation by analysing it from a microdata perspective. In particular, the Fuzzy Double-Hurdle model, a new model which combines the well-known Double-Hurdle model and the fuzzy set theory, is suggested and presented, both technically and by means of a real case study. The proposed model gathers the advantages of the Double-Hurdle model and the fuzzy set theory together producing a suitable model for the analysis of censored observations in presence of imprecise data. Specifically, the Double-Hurdle model allows to efficiently estimate the average values of a non-negative, non-normally distributed variable characterised by high frequency of zero values, as tourists’ expenditure can be, considering the two-stages nature of the decision process. On the other end, the inclusion of the fuzzy set theory in the regression model allows to cope with the imprecision of both collected information (i.e. levels of satisfaction) and kind of measurement used (i.e. Liker-type scale). The results will help tourism managers to more accurately evaluate the efficacy of their policies and marketing strategies in enhancing tourists’ satisfaction and, consequently, in increasing the level of spending at the destination
Trimmed fuzzy clustering of financial time series based on dynamic time warping
No full textIn finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers
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