A framework for clustering uncertain data
Erich Schubert, Alexander Koos, Tobias Emrich, Andreas E Züfle, Klaus Arthur Schmid, Arthur Zimek · Proceedings of the VLDB Endowment · 2015
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.