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Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature , graph classification based on frequent subgraphs is a popular branch: Graphs are(More)
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent(More)
Automatically determining the relative position of a single CT slice within a full body scan provides several useful functionalities. For example, it is possible to validate DICOM meta-data information. Furthermore, knowing the relative position in a scan allows the efficient retrieval of similar slices from the same body region in other volume scans.(More)
Many applications require to determine the k-nearest neighbors for multiple query points simultaneously. This task is known as all-(k)-nearest-neighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations for indexing and query set representation. In this setting, we propose(More)
Medical image repositories contain very large amounts of computer tomography (CT) scans. When querying a particular CT scan, the user is often not interested in the complete scan but in a certain region of interest (ROI). Unfortunately, specifying the ROI in terms of scan coordinates is usually not an option because an ROI is usually specified w.r.t. the(More)
Similarity queries are an important query type in multimedia databases. To implement these types of queries, database systems often use spatial index structures like the R*-Tree. However, the majority of performance evaluations for spatial index structures rely on a conventional background storage layer based on conventional hard drives. Since newer devices(More)
Similarity search and data mining often rely on distance or similarity functions in order to provide meaningful results and semantically meaningful patterns. However, standard distance measures like Lp-norms are often not capable to accurately mirror the expected similarity between two objects. To bridge the so-called semantic gap between feature(More)
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