document: Density-based clustering of uncertain data In many different application areas , e.g. sensor databases , location based services or face recognition systems , distances between odjects have to be computed based on vague and uncertain data . Commonly , the distances between these uncertain object descriptions are expressed by one numerical distance value . Based on such single-valued distance functions 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 . These fuzzy distance functions assign a probability value to each possible distance value . By integrating these fuzzy distance functions directly into data mining algorithms , the full information provided by these functions is exploited . In order to demonstrate the benefits of this general approach , we enhance the density-based clustering algorithm DBSCAN so that it can work directly on these fuzzy distance functions . In a detailed experimental evaluation based on artificial and real-world data sets , we show the characteristics and benefits of our new approach .
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