Conference article

Clustering Geometric Data Streams

Jiri Skala
University of West Bohemia, Czech Republic

Ivana Kolingerova
University of West Bohemia, Czech Republic

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Published in: SIGRAD 2007. The Annual SIGRAD Conference; Special Theme: Computer Graphics in Healthcare; November 28-29; 2007; Uppsala; Sweden

Linköping Electronic Conference Proceedings 28:5, p. 17–23

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Published: 2007-12-20

ISBN: 978-91-7393-990-4

ISSN: 1650-3686 (print), 1650-3740 (online)


Using recent knowledge in data stream clustering we present a modified approach to the facility location problem in the context of geometric data streams. We give insight to the existing algorithm from a less mathematical point of view; focusing on understanding and practical use; namely by computer graphics experts. We propose a modification of the original data stream k-median clustering to solve facility location which is the case when we a priori do not know the number of clusters in the input data. Like the original; the modified version is capable of processing millions of points while using rather small amount of memory. Based on our experiments with clustering geometric data we present suggestions on how to set processing parameters. We also describe how the algorithm handles various distributions of input data within the stream. These findings may be applied back to the original algorithm.


Data stream; clustering; facility location; geometric data


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