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Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All existing approaches, however, are based on an assessment of distances (sometimes indirectly by assuming certain distributions) in the full-dimensional Euclidean data space. In(More)
Traditional clustering algorithms are based on one representation space, usually a vector space. However, in a variety of modern applications, multiple representations exist for each object. Molecules for example are characterized by an amino acid sequence, a secondary structure and a 3D representation. In this paper, we present an efficient density-based(More)
In modern application domains such as multimedia, molecular biology and medical imaging, similarity search in database systems is becoming an increasingly important task. Especially for CAD applications, suitable similarity models can help to reduce the cost of developing and producing new parts by maximizing the reuse of existing parts. Most of the(More)
When automatically extracting information from the world wide web, most established methods focus on spotting single HTML-documents. However, the problem of spotting complete web sites is not handled adequately yet, in spite of its importance for various applications. Therefore, this paper discusses the classification of complete web sites. First, we point(More)
In applications of biometric databases the typical task is to identify individuals according to features which are not exactly known. Reasons for this inexactness are varying measuring techniques or environmental circumstances. Since these circumstances are not necessarily the same when determining the features for different individuals, the exactness might(More)
Over recent years data mining has been establishing itself as one of the major disciplines in computer science with growing industrial impact. Undoubtedly, research in data mining will continue and even increase over coming decades. In this article, we sketch our vision of the future of data mining. Starting from the classic definition of " data mining " ,(More)
The paper is concerned with relation prediction in multi-relational domains using matrix factorization. While most past predic-tive models focussed on one single relation type between two entity types, in the paper a generalized model is presented that is able to deal with an arbitrary number of relation types and entity types in a domain of interest. The(More)
In many companies data is distributed among several sites, i.e. each site generates its own data and manages its own data repository. Analyzing and mining these distributed sources requires distributed data mining techniques to find global patterns representing the complete information. The transmission of the entire local data set is often unacceptable(More)
In many modern applications, there are no exact values available to describe the data objects. Instead, the feature values are considered to be uncertain. This uncertainty is modeled by probability distributions instead of exact feature values. A typical application of such an uncertainty model are moving objects where the exact position of each object can(More)