Michael Wurst

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KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and(More)
We present a freely available benchmark dataset for audio classification and clustering. This dataset consists of 10 seconds samples of 1886 songs obtained from the Garageband site. Beside the audio clips themselves, textual meta data is provided for the individual songs. The songs are classified into 9 genres. In addition to the genre information, our(More)
The overt DIC score of the DIC subcommittee of the ISTH includes a fibrin-related marker (FRM) as indicator of intravascular fibrin formation. The type of marker to be used has not been specified, but D-dimer antigen, or fibrin degradation products are used by most investigators. Soluble fibrin complexes have been suggested as more specific indicators of(More)
OBJECTIVE An abnormality of the optical transmission waveform obtained during measurement of the activated partial thromboplastin time (aPTT) has been described in association with overt disseminated intravascular coagulation. This abnormality, a biphasic waveform, is caused by the in vitro formation of Ca2+-induced complexes between very low density(More)
Smart sensors are increasingly being used to manage and monitor critical urban infrastructures, e.g., for telecommunication, transport, water, or energy networks, as well as for healthcare or smart buildings. Sensor-based monitoring systems offer ways of continuously monitoring low frequency activities, and open the door to new analytic and predictive(More)
Subgroup discovery is a popular form of supervised rule learning, applicable to descriptive and predictive tasks. In this work we study two natural extensions of classical subgroup discovery to distributed settings. In the first variant the goal is to efficiently identify global subgroups, i.e. the rules an analysis would yield after collecting all the data(More)
Knowledge exchange between heterogeneous communities of practice has been recognized as the critical source of innovation and creation of new knowledge. This paper considers the problem of enabling such cross community knowledge exchange through knowledge visualization. We discuss the social nature of knowledge construction and describe main requirements(More)
In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multi-objective and behaves differently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might(More)