Hierarchical density-based clustering of uncertain data

The hierarchical density-based clustering algorithm OPTICS has proven to help the user to get an overview over large data sets. When using OPTICS for analyzing uncertain data which naturally occur in many emerging application areas, e.g. location based services, or sensor databases, the similarity between uncertain objects has to be expressed by one numerical distance value. Based on such single-valued distance functions OPTICS, like other standard data mining algorithms, can work without any changes. In this paper, we propose to express the similarity between two fuzzy objects by distance probability functions which assign a probability value to each possible distance value. Contrary to the traditional approach, we do not extract aggregated values from the fuzzy distance functions but enhance OPTICS so that it can exploit the full information provided by these functions. The resulting algorithm FOPTICS helps the user to get an overview over a large set of fuzzy objects.