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An Abstract Weighting Framework for Clustering Algorithms
Richard Nock and Frank Nielsen
Abstract:
Recent works in unsupervised learning have emphasized the need to understand
a new trend in algorithmic design, which is to influence the clustering via
weights on the instance points.
In this paper, we handle clustering as a constrained minimization
of a Bregman divergence. Theoretical results show benefits resembling
those of boosting algorithms, and bring new modified weighted versions
of clustering algorithms such as k-means, expectation-maximization (EM) and
k-harmonic means. Experiments display the quality of the results
obtained, and corroborate the advantages that subtle data reweightings may bring
to clustering.
Key words:
Statistical/optimization methods, clustering algorithms.
Download the PDF paper here (10 pages, 5 figures) © SIAM.
Bibtex entry:
@InProceedings{nn-awfca-2004,
Author = {Richard Nock and Frank Nielsen},
Title = {An Abstract Weighting Framework for Clustering Algorithms},
Booktitle = {Proceedings of the Fourth International SIAM Conference on Data Mining},
Year = 2004,
Month = {April},
Publisher = {SIAM},
Address = "Orlando, FL, USA",
Pages = "200-209"
}
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Last updated, 2004.