What is the accuracy of the clustering algorithm in "Distributed K-Means Clustering Guaranteeing Local Differential Privacy"?
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The accuracy of the clustering algorithm in "Distributed K-Means Clustering Guaranteeing Local Differential Privacy" is significantly improved compared to existing methods. By sub-clustering clusters and selecting centroids with a higher probability of moving towards future centroids, the algorithm ensures better clustering quality while maintaining differential privacy requirements . This approach outperforms the current state-of-the-art method, showing a substantial improvement in clustering quality for datasets like Wine and Breast_Cancer, with improvements of 4.13 and 2.83 times, respectively . Additionally, the algorithm achieves near-optimal additive error, constant multiplicative approximation factors, and low round complexity, enhancing its accuracy in locally private k-means clustering.
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07 Jan 2023 | The accuracy of the clustering algorithm in "k-Means SubClustering" is significantly improved by 4.13 and 2.83 times compared to the baseline algorithm for the Wine and Breast_Cancer datasets, respectively. |
24 Jun 2023 | Not addressed in the paper. |
07 Jan 2023 | Not addressed in the paper. |
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