Frequent subgraph mining is an important data-mining technique. In this paper we look at weighted graphs, which are ubiquitous in the real world. The analysis of weights in combination with mining for substructures might yield more precise results. In particular, we study frequent subgraph mining in the presence of weight-based constraints and explain how to integrate them into mining algorithm...

OBJECTIVES Attitudes towards communication skills of medical undergraduates can be gathered using the Communication Skills Attitude Scale (CSAS). We aimed to develop a German version of the CSAS (CSAS-G) in order to explore attitudes towards communication skills in a German cohort. Additionally the potential influence of demographic factors was examined. METHODS We realized the CSAS-G and con...

Detecting references and similarities in music lyrics can be a difficult task. Crowdsourced knowledge platforms such as Genius. help this process through user-annotated information about the artist song but fail to include visualizations users find structures on higher more abstract level. We propose prototype compute between rap artists based word embedding of their crawled from Furthermore, a...

We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorativelearning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations that are similar to continuous optimization methods. Our theoretical contributio...

Purpose – Innovation within clusters is a crucial aspect in regional development. In the last decades, in the literature the role of absorptive capacity, environment, trust and cooperation in leveraging both explorative and exploitive innovation has been highlighted. In this paper we assess the contributions of these factors on the basis of a comparative analysis between about 250 firms belongi...

Decades ago, increased volume of data made manual analysis obsolete and prompted the use of computational tools with interactive user interfaces and rich palette of data visualizations. Yet their classic, desktop-based architectures can no longer cope with the ever-growing size and complexity of data. Next-generation systems for explorative data analysis will be developed on client–server archi...

This paper describes a method of explorative data analysis which allows to detect logical implications between items of a dichotomous questionnaire or test. These logical dependencies are organized to form a hierarchical structure (quasi-order) on the items. Our analysis method, which is called Inductive Item Tree Analysis, can be seen as a method of Boolean analysis. We discuss the relation of...