DBDC: Density Based Distributed Clustering

Abstract. Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. In this paper, we propose a different approach. We cluster the data locally and extract suitable representatives from these clusters. These representatives are sent to a global server site where we restore the complete clustering based on the local representatives. This approach is very efficient, because the local clustering can be carried out quickly and independently from each other. Furthermore, we have low transmission cost, as the number of transmitted representatives is much smaller than the cardinality of the complete data set. Based on this small number of representatives, the global clustering can be done very...

• Type: Conference
• Year: 2004
• Where: EDBT
• Authors: Eshref Januzaj, Hans-Peter Kriegel, Martin Pfeifle
• Keywords: Complete Clustering, Database, EDBT 2004, Global Clustering, Local Clustering