Density-Based Clustering of Uncertain Data

Speaker: CHUI Chun Kit

Abstract

In many different application areas, e.g. sensor databases, location based services or face recognition systems, distances between objects 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. The authors of this paper propose to express the similarity between two fizzy 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, the authors 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, the authors show the characteristics and benefits of their new approach.