A Method for Autonomous Data Partitioning

Xiaowei Gu, Plamen Parvanov Angelov, Jose Principe

Research output: Contribution to journalArticlepeer-review

43 Citations (SciVal)
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In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms.
Original languageEnglish
Pages (from-to)65-82
Number of pages18
JournalInformation Sciences
Early online date17 May 2018
Publication statusPublished - 01 Sept 2018
Externally publishedYes


  • Autonomous
  • Data partitioning
  • Local modes
  • Voronoi tessellation


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