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A Robust Multi-Sphere SVC Algorithm Based on Parameter Estimation

Published: 19 January 2022 Publication History

Abstract

To improve the robustness to noise, outliers and arbitrary cluster boundaries, a robust multi-sphere support vector clustering (SVC) algorithm is proposed in this paper. The proposed algorithm can automatically estimate a suitable kernel parameter, and determine the cluster number. The Gaussian kernel parameter is firstly estimated through a kernel parameter estimation algorithm which is based on support vector domain description (SVDD) and original local variance (LV) algorithm. Based on the estimated kernel parameter, the SVC algorithm classifies the given data points into different clusters and then the SVDD algorithm is performed several times for each cluster. At last, the membership is computed and the final clustering result is obtained based on these computed memberships. Several simulations verify the effectiveness of the proposed algorithm.

References

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Liang Sun, Shinichi Yoshida, Yan chun Liang. A novel support vector and K-means based Hybrid clustering algorithm. Proceedings of the 2010 IEEE international conference on information and automation, 2010:126-130.
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      cover image ACM Other conferences
      AISS '21: Proceedings of the 3rd International Conference on Advanced Information Science and System
      November 2021
      526 pages
      ISBN:9781450385862
      DOI:10.1145/3503047
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 19 January 2022

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      Author Tags

      1. kernel parameter estimation
      2. local variance
      3. noise and outliers
      4. support vector clustering
      5. support vector domain description

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