Abstract
K-means algorithm for data mining is combined with differential privacy preservation. Although it improves the security of data information, the selection of clustering number and initial center point is still blind and random. In this paper, we integrate an optimized Canopy algorithm with DP K-means algorithm, and apply it to Hadoop platform. Firstly, we optimize the Canopy algorithm according to the minimum and maximum principle and use the functions of the MapReduce framework to implement it. Secondly, we utilize the number and the set of center points obtained to implement the DP K-means algorithm on MapReduce. As a result, the improved Canopy algorithm can optimize the selection of the number of centers and clusters on Hadoop platform, so the proposed K-means algorithm can improve security, usability and efficiency of calculation.
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Acknowledgment
Project supported by the National Key Research and Development Program of China (No. 2016YFC1000307) and the National Natural Science Foundation of China (No. 61571024) for valuable helps.
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Shang, T., Zhao, Z., Guan, Z., Liu, J. (2017). A DP Canopy K-Means Algorithm for Privacy Preservation of Hadoop Platform. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_14
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DOI: https://doi.org/10.1007/978-3-319-69471-9_14
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