Clustering Oligarchies

Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We investigate the extent to which clustering algorithms are robust to the addition of a small, potentially adversarial, set of points. Our analysis reveals radical differences in the robustness of popular clustering methods. k-means and several related techniques are robust when data is clusterable, and we provide a quantitative analysis capturing the precise relationship between clusterability and robustness. In contrast, common linkage-based algorithms and several standard objective-function-based clustering methods can be highly sensitive to the addition of a small set of points even when the data is highly clusterable. We call such sets of points oligarchies. Lastly, we show that the behavior with respect to oligarchies of the popular Lloyd’s method changes radically with the initialization technique.
Original languageEnglish
Title of host publicationProceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, April 29 - May 1, 2013
PublisherJMLR.org
Pages66-74
Number of pages9
Volume31
StatePublished - 2013
Externally publishedYes
Event16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States
Duration: 29 Apr 20131 May 2013

Publication series

NameJMLR Workshop and Conference Proceedings
PublisherJMLR.org

Conference

Conference16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013
Country/TerritoryUnited States
CityScottsdale
Period29/04/131/05/13

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