Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Robust k-means: a theoretical revisit

Georgogiannis Alexandros

Simple record

URIhttp://purl.tuc.gr/dl/dias/25D438D5-51A7-4B8D-9E6D-1D1B4A108D81-
Identifierhttps://papers.nips.cc/paper/6126-robust-k-means-a-theoretical-revisit-
Languageen-
Extent9 pagesen
TitleRobust k-means: a theoretical revisiten
CreatorGeorgogiannis Alexandrosen
CreatorΓεωργογιαννης Αλεξανδροςel
PublisherNeural information processing systems foundationen
Content SummaryOver the last years, many variations of the quadratic k-means clustering procedure have been proposed, all aiming to robustify the performance of the algorithm in the presence of outliers. In general terms, two main approaches have been developed: one based on penalized regularization methods, and one based on trimming functions. In this work, we present a theoretical analysis of the robustness and consistency properties of a variant of the classical quadratic k-means algorithm, the robust k-means, which borrows ideas from outlier detection in regression. We show that two outliers in a dataset are enough to breakdown this clustering procedure. However, if we focus on "well-structured" datasets, then robust k-means can recover the underlying cluster structure in spite of the outliers. Finally, we show that, with slight modifications, the most general non-asymptotic results for consistency of quadratic k-means remain valid for this robust variant.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-10-26-
Date of Publication2016-
Subjectk-means clusteringen
Bibliographic CitationA. Georgogiannis, "Robust k-means: a theoretical revisit," in 30th Annual Conference on Neural Information Processing Systems, 2016, pp. 2891-2899.en

Services

Statistics

 

Copyright © DIAS 2013