Abstract
The existing building surface data fusion algorithms do not extract the segmented data features, resulting in inaccurate fusion results. In heterogeneous environment, a Clustering Fusion Algorithm Based on mutual information and fractal dimension is proposed. The regression coefficient is used to express the sequence, and the data feature representation and data dimension reduction are realized. The dynamic data series are processed by similarity measure function method. For the long dynamic data series, the piecewise aggregation approximation method is used to segment the data and then extract the features. Through the incremental clustering processing data based on fractal dimension clustering algorithm, the research of data fusion algorithm is realized. The experimental results show that the accuracy of building surface data fusion is greatly improved by using the dynamic data fusion algorithm, the highest is 0.98, the sum of square error is reduced, and the lowest is only 90.44.
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This paper is supported by the Major projects of science and technology in Inner Mongolia with No. 2019ZD016.
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Zhu, J., Gao, J. Dynamic Fusion Algorithm of Building Surface Data in Heterogeneous Environment. Mobile Netw Appl 26, 449–458 (2021). https://doi.org/10.1007/s11036-020-01677-2
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DOI: https://doi.org/10.1007/s11036-020-01677-2