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Abstract: Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance function satisfying the triangle inequality; this distance function along with the collection of objects describes a distance space. In a distance space, the only operation possible on data objects is the computation of distance between them. All scalable algorithms in the literature assume a special type of distance... (Update)
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BibTeX entry: (Update)
V. Ganti, R. Ramakrishnan, J. Gehrke, A. Powell, and J. French. Clustering large datasets in arbitrary metric spaces. Technical report, University of Wisconsin-Madison, 1998. http://citeseer.ist.psu.edu/ganti99clustering.html More
@inproceedings{ ganti99clustering,
author = "Venkatesh Ganti and Raghu Ramakrishnan and Johannes Gehrke and Allison L. Powell and James C. French",
title = "Clustering Large Datasets in Arbitrary Metric Spaces",
booktitle = "{ICDE}",
pages = "502-511",
year = "1999",
url = "citeseer.ist.psu.edu/ganti99clustering.html" }
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