Efficient Indexing of Complex Objects for Density-based Clustering
Karin Kailing, Hans‐Peter Kriegel, Martin Pfeifle · 2004
Databases are getting more and more important for storing complex objects from scientific, engineering or multimedia applications. Examples for such data are chemical compounds, CAD drawings or XML data. The efficient search for similar objects in such databases is a key feature. However, the general problem of many similarity measures for complex objects is their computational complexity, which makes them unusable for large databases. An area where this complexity problem is a strong handicap is that of densitybased clustering where many similarity range queries have to be performed. In this paper, we combine and extend the two techniques of metric index structures and multistep query processing to improve the performance of range query processing. The efficiency of our methods is demonstrated in extensive experiments on real world data including graphs, trees and vector sets.