Digital Library[ Search Result ]
Deep k-Means Node Clustering Based on Graph Neural Networks
http://doi.org/10.5626/JOK.2023.50.12.1153
Recently, graph node clustering techniques using graph neural networks (GNNs) have been actively studied. Notably, most of these studies use a GNN to embed each node into a low-dimensional vector and then cluster the embedding vectors using the existing clustering algorithms. However, since this approach does not consider the final goal of clustering when training the GNN, it is difficult to say that it produces optimal clustering results. Therefore, in this paper, we propose a deep k-means clustering method that iteratively trains a GNN considering the final goal of k-means clustering and performs k-means clustering on the embedding vectors generated by the trained GNN. The proposed method considers both the similarity between nodes and the loss of k-means clustering when training a GNN. Experimental results using real datasets confirmed that the proposed method improves the quality of k-means clustering results compared to the existing methods.
An Efficient and Differentially Private K-Means Clustering Algorithm Using the Voronoi Diagram
http://doi.org/10.5626/JOK.2020.47.9.879
Studies have been recently conducted on preventing the leakage of personal information from the analysis results of data. Among them, differential privacy is a widely studied standard since it guarantees rigorous and provable privacy preservation. In this paper, we propose an algorithm based on the Voronoi diagram to publish the results of the K-means clustering for 2D data while guaranteeing the differential privacy. Existing algorithms have a disadvantage in that it is difficult to select the number of samples for the data since the running time and the accuracy of the clustering results may change according to the number of samples. The proposed algorithm, however, could quickly provide an accurate clustering result without requiring such a parameter. We also demonstrate the performance of the proposed algorithm through experiments using real-life data.
Differentially Private k-Means Clustering based on Dynamic Space Partitioning using a Quad-Tree
Hanjun Goo, Woohwan Jung, Seongwoong Oh, Suyong Kwon, Kyuseok Shim
http://doi.org/10.5626/JOK.2018.45.3.288
There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker’s background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr