The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty-using different uncertainty models-on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.

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This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.

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Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from geo-textual travel blogs, that define closeness between pairs of points of interest (POIs) and quantify each of these relations using a probabilistic model. Using Bayesian inference, we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we derive an altered cost function taking crowdsourced spatial relations into account. We propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -based on real world datasetsshow that the computed paths yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.