A Quality Measure for Distributed Clustering

Clustering has become an increasingly important task in modern application domains. Mostly, 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. Another approach is to cluster the data locally and extract suitable representatives from these clusters. Based on these representatives a global server tries to reconstruct the complete clustering. In this paper, we discuss the complex problem of finding a suitable quality measure for evaluating the quality of such a distributed clustering. We introduce a discrete and continuous quality criterion which we empirically compare to each other. Keywords Distributed Data Mining, partitioning clustering, quality measure for distributed clustering

• Type: Conference
• Year: 2004
• Where: DBA
• Authors: Eshref Januzaj, Hans-Peter Kriegel, Martin Pfeifle
• Keywords: Complete Clustering, Database, DBA 2004, Modern Application Domains, Quality Measure