Clustering is the problem of partitioning data into a finite number k of homogeneous and separate groups, called clusters. A good choice of k is essential for building meaningful clusters. In this paper, this task is addressed from the point of view of model selection via penalization. We design...

In this paper we propose an unsupervised voting-merging scheme that is capable of clustering data sets, and also of finding the number of clusters existing in them. The voting part of the algorithm allows us to combine several runs of clustering algorithms resulting in a common partition.(...)

Clusters of infected individuals are defined on data from health laboratories, but this quantity has not been defined and characterized by epidemy models on statistical physics. For a system of mobile agents we simulate a model of infection without immunization and show that all the moments of...

In this paper we introduce a mixed approach for the semi-supervised data problem. Our approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters.(...)

Wirft man einen Blick auf die Veränderungen der Agrarstrukturen in den letzten Dekaden,so fällt einem unweigerlich die Entwicklung Nordwestdeutschlands auf. Auseinem Gebiet, zum Teil geprägt durch „Ungunsträume“ und Selbstversorgungsstrukturen(Windhorst und Klohn, 1995; Campos, 1992),...

In der Humangeographie gewinnen die Konzepte zu regionalen Branchenclustern immergrößere Bedeutung. In diesen Konzepten arbeiten Unternehmen aus der gleichen oderähnlichen Wertschöpfungsketten zusammen in einer Region mit identischem sozialem undkulturellem Hintergrund. Aufgrund ihrer...

In this paper we use a novel approach and a large Portuguese employer-employee paneldata set to study the hypothesis that industrial agglomeration improves the quality of the firmworkermatching process. Our method makes use of recent developments in the estimationand analysis of models with...