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  4. Vol 6, Issue 7, 2022

Distributed Privacy-preserving Clustering Mining Algorithm for Heterogeneous Computing

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DOI: 10.23977/acss.2022.060707 | Downloads: 21 | Views: 490

Author(s)

Jing Qi 1

Affiliation(s)

1 College of Intelligent Equipment, Shandong University of Science and Technology, Taian, Shandong, 271019, China

Corresponding Author

Jing Qi

ABSTRACT

As a typical unsupervised data mining method, cluster analysis can mine unknown knowledge and potential value from massive data. However, in the process of mining useful information, the personal privacy information in the data may be leaked. Therefore, privacy protection technology comes into being. This paper focuses on the distributed privacy preserving clustering mining algorithm for heterogeneous computing. This thesis first constructs a mathematical model based on heterogeneous Hadoop. In order to further improve the availability of the algorithm, an effective algorithm DPk means ev is studied and proposed. The algorithm improves the selection of the initial central point and avoids the blindness of the value setting and the sensitivity of the initial central point selection. The experimental results show that the algorithm effectively improves the efficiency and availability of clustering.

KEYWORDS

Heterogeneous Computing, Distributed Architecture, Privacy Protection, Clustering Algorithm

CITE THIS PAPER

Jing Qi, Distributed Privacy-preserving Clustering Mining Algorithm for Heterogeneous Computing. Advances in Computer, Signals and Systems (2022) Vol. 6: 44-51. DOI: http://dx.doi.org/10.23977/acss.2022.060707.

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