Fast support vector clustering

Tung Pham, Trung Le, Thai Hoang Le, Dat Tran

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


Support-based clustering has recently drawn plenty of attention because of its applications in solving the diffcult and diverse clustering or outlier detection problem. Support-based clustering method undergoes two phases: finding the domain of novelty and doing clustering assignment. To find the domain of novelty, the training time given by the current solvers is typically quadratic in the training size. It precludes the usage of support-based clustering method for the large-scale datasets. In this paper, we propose applying Stochastic Gradient Descent framework to the first phase of support-based clustering for finding the domain of novelty in form of a half-space and a new strategy to do the clustering assignment. We validate our proposed method on the well-known datasets for clustering to show that the proposed method offers a comparable clustering quality to Support Vector Clustering while being faster than this method.

Original languageEnglish
Title of host publicationESANN 2016 - 24th European Symposium on Artificial Neural Networks
Subtitle of host publicationBruges, Belgium, April 27-28-29
EditorsFrançois Blayo, Gianluca Bontempi, Marie Cottrell, Mia Loccufier, Bernard Manderick, Jean-Pierre Peters, Johan Suykens, Joos Vandewalle, Michel Verleysen, Louis Wehenkel
Place of PublicationLouvain la Neuve Belgium
Number of pages6
ISBN (Electronic)9782875870278
Publication statusPublished - 2016
Externally publishedYes
EventEuropean Symposium on Artificial Neural Networks 2016 - Bruges, Belgium
Duration: 27 Apr 201629 Apr 2016
Conference number: 24th


ConferenceEuropean Symposium on Artificial Neural Networks 2016
Abbreviated titleESANN 2016
Internet address

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