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Abstract: The problem of assigning m points in the n-dimensional real space to k clusters is formulated as that of determining k centers in such that the sum of distances of each point to the nearest center is minimized. If a polyhedral distance is used, the problem can be formulated as that of minimizing a piecewise-linear concave function on a polyhedral set which is shown to be equivalent to a bilinear program: minimizing a bilinear function on a polyhedral set. A fast finite k-Median... (Update)
Context of citations to this paper: More
...of the data. Clustering may be performed using methods such as K means [18] expectation maximization (EM) 16] or optimization models [8]. Recently a set of novel clustering algorithms have been proposed in the database community [26,55] For instance, Agrawal et al. 1]...
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BibTeX entry: (Update)
P. S. Bradley, O. L. Mangasarian, and W. N. Street. Clustering via concave minimization. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems -9-, pages 368--374, Cambridge, MA, 1997. MIT Press. ftp://ftp.cs.wisc.edu/mathprog /tech-reports/96-03.ps.Z. http://citeseer.ist.psu.edu/bradley97clustering.html More
@inproceedings{ bradley97clustering,
author = "P. S. Bradley and O. L. Mangasarian and W. N. Street",
title = "Clustering via Concave Minimization",
booktitle = "Advances in Neural Information Processing Systems",
volume = "9",
publisher = "The {MIT} Press",
editor = "Michael C. Mozer and Michael I. Jordan and Thomas Petsche",
pages = "368",
year = "1997",
url = "citeseer.ist.psu.edu/bradley97clustering.html" }
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