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Uncertain Data Clustering Based on Probability Distribution in Obstacle Space

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Abstract

In the case of current technology, most of the measurements are focused on geometric distance, and the distribution of data is not considered. In order to compensate for this shortcoming of geometric distance measurement, this paper uses the KL distance as the similarity measurement standard for uncertain data, and the DOUD_C algorithm and COUD_C algorithm are proposed respectively in the discrete domain and continuous domain. In order to solve the problem of efficient clustering of the high dimensional data, this paper considers the data structure of grid, and BROUD_C algorithm is proposed. According to the adjacency characteristic of the grid, the cluster process is extended continuously, the algorithm can find clusters of arbitrary shapes, and we can filter a large number of isolated points, it solves the uncertain data clustering problem effectively in the obstacle space. The experimental results show that compared to the OBS_UK_means with VPA and SDA pruning algorithm and FOPTICS algorithm, the clustering performance of BROUD_C algorithm is more significant and CPU has less execution time in the obstacle space.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61370084; the Science and Technology Research Project of Heilongjiang Provincial Education Department 1253lz004.

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  1. College of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Jing Wan, Meiyu Cui, Yunbin He & Song Li

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  1. Jing Wan
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  2. Meiyu Cui
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Wan, J., Cui, M., He, Y. et al. Uncertain Data Clustering Based on Probability Distribution in Obstacle Space. Wireless Pers Commun 111, 2191–2214 (2020). https://doi.org/10.1007/s11277-019-06980-0

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