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The density-based clustering algorithm DBSCAN is a fundamental technique for data clustering with many attractive properties and applications. However, DBSCAN requires specifying all pair wise (dis)similarities among objects that can be non-trivial to obtain in many applications. To tackle this problem, in this paper, we propose a novel active density-based(More)
Data volume and complexity continue to increase, as does the need for insight into data. Today, data management and data analytics are most often conducted in separate systems: database systems and dedicated analytics systems. This separation leads to timeand resource-consuming data transfer, stale data, and complex IT architectures. In this paper we show(More)
How can we retrieve information from sparse graphs? Traditional graph mining approaches focus on discovering dense patterns inside complex networks, for example modularity-based or cut-based methods. However, most real world data sets are very sparse. Nevertheless, traditional approaches tend to omit interesting sparse patterns like stars. In this paper, we(More)
Zusammenfassung. Die Nutzung komplexer Algorithmen zur Analyse oft hochdimensionaler Datensätze gerät im Kontext von " Big Data " immer mehr in das Zentrum der Aufmerksamkeit. Um diese komple-xen Datenanalysen effizient zu ermöglichen liegt es nahe, sie in die am weitesten verbreiteten Datenspeicher zu integrieren – in relationale Da-tenbanksysteme. Dies(More)
Social networks play an important role in Web2.0. Formanyusers establishing contacts and staying in touch in the virtual world is more than just aspare time filler.I ns ocial networks likeF acebook theyp rovide much information about themselves in user profiles. Also for online dating the focus is on establishing newcontacts. In general, three types of(More)
Data can encapsulate different object groupings in subspaces of arbitrary dimension and orientation. Finding such subspaces and the groupings within them is the goal of generalized subspace clustering. In this work we present a generalized subspace clustering technique capable of finding multiple non-redundant clusterings in arbitrarily-oriented subspaces.(More)
Data measured in wireless sensor networks are inherently imprecise, due to a number of reasons, and aggregate queries are often used to analyze the collected data in order to alleviate the impact of such imprecision. In this paper we will deal with the imprecision in the measured values explicitly by employing a probabilistic approach and we focus on one(More)