Gu, Xiaowei and Angelov, Plamen Parvanov and Principe, Jose (2018) A Method for Autonomous Data Partitioning. Information Sciences, 460-46. pp. 65-82. ISSN 0020-0255
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Abstract
In this paper, we propose a fully autonomous, non-parametric, data partitioning algorithm, which is able to automatically recognize local maxima of the density from empirical observations and use them as the focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. It 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.