Improvement of Differential Privacy K-means Clustering Algorithm

Improvement of Differential Privacy K-means Clustering Algorithm

In order to address the issues of arbitrary center selection and unreasonable privacy budget allocation leading to poor clustering performance in differential privacy K-means clustering algorithm, a new center selection scheme is designed based on two principles for initial center selection. By calc...

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Bibliographic Details
Main Authors: GUO Rumin, CHEN Xuebin, SHAN Liyang
Format: Article
Language:Chinese
Published: Harbin University of Science and Technology Publications 2024-08-01
Series:Journal of Harbin University of Science and Technology
Subjects:
privacy preserving techniques
k-means clustering
clustering algorithms
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2345
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