Clustering confidence sets

In this article, we present a novel approach to clustering finite or infinite dimensional objects observed with different uncertainty levels. The novelty lies in using confidence sets rather than point estimates to obtain cluster membership and the number of clusters based on the distance between the confidence set estimates. The minimal and maximal distances between the confidence set estimates provide confidence intervals for the true distances between objects. The upper bounds of these confidence intervals can be used to minimize the within clustering variability and the lower bounds can be used to maximize the between clustering variability. We assign objects to the same cluster based on a min–max criterion and we separate clusters based on a max–min criterion. We illustrate our technique by clustering a large number of curves and evaluate our clustering procedure with a synthetic example and with a specific application.     

Organizational Communication-Strategies at International Level

Human resources management represents the management of a company’s workforce. Human resources is not just a simple resource, it is also an important investment for companies. At the international level, communication in the management of human resources is more important because attraction, selection and training is not just for the regional level and needs to be adapted to international standards. We are trying to investigate in the first part of our research the character of organizational communication, based on human resources and to explain the strategies that can be used in organizational communication nowadays. In the second part of our research we analyze the content of successful organizational communication at the international level.