Dutch-American comparisons of the ’sense of political efficacy’: some remarks on cross-cultural ’robustness’

The concept of ’robustness’ of scales in cross-cultural comparison is introduced and used in a comparison of the properties of the well-known scale of ’sense of political efficacy for American and Dutch data. The same scale properties are compared across several cultural subgroupings within the United States. On the basis of a version of Guttman type scale analysis applied by the author, an improved scale possessing some degree of cross-cultural robustness is constructed. A type of «-alp construction, devised by the author, was used for the construction of an extended scale for use in the Netherlands. Finally the paper presents this new Dutch scale of political efficacy, consisting of nine item

Density-based unsupervised classification for remote sensing

Most image classification methods are supervised and use a parametric model of the classes that have to be detected. The models of the different classes are trained by means of a set of training regions that usually have to be marked and classified by a human interpreter. Unsupervised classification methods are data-driven methods that do not use such a set of training samples. Instead, these methods look for (repeated) structures in the data. In this paper we describe a non-parametric unsupervised classification method. The method uses biased sampling to obtain a learning sample with little noise. Next, density estimation based clustering is used to find the structure in the learning data. The method generates a non-parametric model for each of the classes and uses these models to classify the pixels in the image

An algorithm to discover the k-clique cover in networks

In social network analysis, a k-clique is a relaxed clique, i.e., a k-clique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented