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Abstract: Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework applicable to a wide class of iterative clustering. We require at most one scan of the database. In this work, the framework is instantiated and numerically justified with the popular K-Means clustering algorithm. The method is based on identifying regions of the data that are compressible, regions that... (Update)
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BibTeX entry: (Update)
P. S. Bradley, U. Fayyad, and C. Reina, "Scaling Clustering Algorithms to Large Databases", To appear, Proc. 4 th International Conf. on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, Aug. 1998. http://citeseer.ist.psu.edu/bradley98scaling.html More
@inproceedings{ bradley98scaling,
author = "Paul S. Bradley and Usama M. Fayyad and Cory Reina",
title = "Scaling Clustering Algorithms to Large Databases",
booktitle = "Knowledge Discovery and Data Mining",
pages = "9-15",
year = "1998",
url = "citeseer.ist.psu.edu/bradley98scaling.html" }
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