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.(...)

This paper we deals with a fuzzy voting scheme for cluster algorithms.

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.(...)

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable,...

When running agent-based simulations using ready-made components, one usually faces heterogenity problems both for the agents implementation and for the underlying platform. To circumvent these kind of hindrances, we introduce a wrapper technique for mapping the functionality of agents living in...

Autoren-Abstract: This paper introduces ideas and methods for testing for structural change in linear regression models and presents how these have been realized in an R package called strucchange. It features tests from the generalized fluctuation test framework as well as from the F test (Chow...

In this study, we empirically analyze the determinants of heterogeneity in rating assessments across different segments of the European loan market. We conduct a benchmarking analysis using rating information on European corporate obligors from nine major Austrian banks that have a large share...