Local modes-based free-shape data partitioning

Plamen Parvanov Angelov, Xiaowei Gu

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE Press
ISBN (Electronic)9781509042401
ISBN (Print)9781509042418
Publication statusPublished - 13 Feb 2017
Externally publishedYes

Publication series

Name2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016


  • data cloud
  • data partitioning
  • data- driven
  • evolving clustering
  • parameterfree


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