Voronoi Cell-Based Clustering Using a Kernel Support

Kyoungok Kim, Youngdoo Son, Jaewook Lee

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Support-based clustering using kernels suffers from serious computational limitations inherent in many kernel methods when applied to very large-scale problems despite its ability to identify clusters with complex shapes. In this paper, we propose a novel clustering algorithm called Voronoi cell-based clustering to expedite support-based clustering using kernels. In contrast to previous studies, including the basin cell-based method, the proposed method achieves computational efficiency in both the training phase to construct a support estimate using sampled data to reduce the evaluation of kernels and the labeling phase to assign a cluster label on each data point nearest its representative point. The performance superiority of the proposed method over the other basin cell-based methods in terms of computational time and storage efficiency is verified by various experiments using benchmark sets and in real applications to image segmentation.

Original languageEnglish
Article number6906252
Pages (from-to)1146-1156
Number of pages11
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number4
DOIs
StatePublished - 1 Apr 2015

Keywords

  • Clustering
  • kernel methods
  • support level function

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