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n3:EPrint bibo:Article n3:ArticleEPrint bibo:AcademicArticle

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n9:j.ins.2018.05.030

dcterms:title

A method for autonomous data partitioning

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dcterms:date

2018-09-01

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n10:ext-11ab9d61306db2fc84b554e5d5089af5 n10:ext-2ec95e5afa7a3687b87151cb8654e0cd n10:ext-x.gu@kent.ac.uk

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n7:ext-f308aad1ef8f70546c3a197f104f2ad5

bibo:abstract

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.

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n12:QA75

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bibo:volume

460-46