Prof. Dr.

Wrobel, Stefan

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Now showing 1 - 4 of 4
  • Publication
    Effective approximation of parametrized closure systems over transactional data streams
    ( 2020)
    Trabold, Daniel
    ;
    Horvath, Tamas
    ;
    Wrobel, Stefan
    Strongly closed itemsets, defined by a parameterized closure operator, are a generalization of ordinary closed itemsets. Depending on the strength of closedness, the family of strongly closed itemsets typically forms a tiny subfamily of ordinary closed itemsets that is stable against changes in the input. In this paper we consider the problem of mining strongly closed itemsets from transactional data streams. Utilizing their algebraic and algorithmic properties, we propose an algorithm based on reservoir sampling for approximating this type of itemsets in the landmark streaming setting, prove its correctness, and show empirically that it yields a considerable speed-up over a straightforward naive algorithm without any significant loss in precision and recall. We motivate the problem setting considered by two practical applications. In particular, we first experimentally demonstrate that the above properties, i.e., compactness and stability, make strongly closed itemsets an excellent indicator of certain types of concept drifts in transactional data streams. As a second application we consider computer-aided product configuration, a real-world problem raised by an industrial project. For this problem, which is essentially exact concept identification, we propose a learning algorithm based on a certain type of subset queries formed by strongly closed itemsets and show on real-world datasets that it requires significantly less query evaluations than a naive algorithm based on membership queries.
  • Publication
    Frequent subgraph mining in outerplanar graphs
    ( 2010)
    Horvath, Tamas
    ;
    Ramon, J.
    ;
    Wrobel, Stefan
    In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we consider the class of outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for outerplanar graphs, and show that it works in incremental polynomial time for the practically relevant subclass of well-behaved outerplanar graphs, i.e., which have only polynomially many simple cycles. We evaluate the algorithm empirically on chemo- and bioinformatics applications.
  • Publication
    Efficient discovery of interesting patterns based on strong closedness
    ( 2009)
    Boley, Mario
    ;
    Horvath, Tamas
    ;
    Wrobel, Stefan
    Finding patterns that are interesting to a user in a certain application context is one of the central goals of data mining research. Regarding all patterns above a certain frequency threshold as interesting is one way of defining interestingness. In this paper, however, we argue that in many applications, a different notion of interestingness is required in order to be able to capture "long", and thus particularly informative, patterns that are correspondingly of low frequency. To identify such patterns, our proposed measure of interestingness is based on the degree or strength of closedness of the patterns. We show that (i) indeed this definition selects long interesting patterns that are difficult to identify with frequency-based approaches, and (ii) that it selects patterns that are robust against noise and/or dynamic changes. We prove that the family of interesting patterns proposed here forms a closure system and use the corresponding closure operator to design a mining algorithm listing these patterns in amortized quadratic time. In particular, for nonsparse datasets its time complexity is O(nm) per pattern, where n denotes the number of items and m the size of the database. This is equal to the best known time bound for listing ordinary closed frequent sets, which is a special case of our problem. We also report empirical results with real-world datasets.
  • Publication
    Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds
    ( 2008)
    Geppert, H.
    ;
    Horvath, Tamas
    ;
    Gärtner, Thomas
    ;
    Wrobel, Stefan
    ;
    Bajorath, J.
    Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.