Multi-step density-based clustering

Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex distance measures are first choice but also simpler distance functions are available which can be computed much more efficiently. In this paper, we will demonstrate how the paradigm of multi-step query processing which relies on exact as well as on lower-bounding approximated distance functions can be integrated into the two density-based clustering algorithms DBSCAN and OPTICS resulting in a considerable efficiency boost. Our approach tries to confine itself to -range queries on the simple distance functions and carries out complex distance computations only at that stage of the clustering algorithm where they are compulsory to compute the correct clustering result. Furthermore, we will show how our approach can be used for approximated clustering allowing the user to find an individual tradeoff between quality a...

• Type: Journal
• Year: 2006
• Where: KAIS
• Authors: Stefan Brecheisen, Hans-Peter Kriegel, Martin Pfeifle
• Keywords: Clustering Algorithm, Complex Distance, Distance Functions, KAIS 2006