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Online domain description of big data based on hyperellipsoid models

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Abstract

Big data is usually massive, diverse, time-varying, and high-dimensional. The focus of this paper is on the domain description of big data, which is the basis for solving the above problems. This paper has three main contributions. Firstly, one hyperellipsoid model is proposed to analyze domain description of big data. The parameters of the hyperellipsoid model can be adaptively adjusted according to the proposed objective function without relying on manual parameter selection, which expands the application range of the model. Secondly, an improved FDPC algorithm is proposed to generate multiple hyperellipsoid models to approximate the spatial distribution of big data, thus improving the accuracy of domain description. Multiple hyperellipsoid models can not only greatly eliminate the spatial redundancy of the domain description based on one hyperellipsoid model, but also provide a feasible method for describing complex spatial distribution. Thirdly, an online domain description algorithm based on hyperellipsoid models is proposed, which improves the robustness of hyperellipsoid models on time-varying data. The parallel processing flow of the algorithm is given. In the experiment, synthetic instances and real-world datasets were applied to test the performance of hyperellipsoid models. By comparing LOF, OneClassSVM, SVDD and isolation forest, the performance of the proposed method is competitive and promising.

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  1. Jiangnan University, Wuxi, Jiangsu, China

    Zengshuai Qiu

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  1. Zengshuai Qiu
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Correspondence to Zengshuai Qiu.

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Qiu, Z. Online domain description of big data based on hyperellipsoid models. Int. J. Mach. Learn. & Cyber. 12, 2185–2197 (2021). https://doi.org/10.1007/s13042-021-01300-0

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  • DOI: https://doi.org/10.1007/s13042-021-01300-0

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