Name
Abstract | ||
|---|---|---|
Kernel k-means is a useful way to identify clusters for nonlinearly separable data. Solving the kernel k-means problem is time consuming due to the quadratic computational complexity. Outsourcing the computations of solving kernel k-means to external cloud computing service providers benefits the data owner who has only limited computing resources. However, data privacy is a critical concern in outsourcing since the data may contain sensitive information. In this paper, we propose a method for privacy-preserving outsourcing of kernel k-means based on the randomized kernel matrix. The experimental results show that the clustering performance of the proposed randomized kernel k-means is similar to a normal kernel k-means algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDMW.2013.29 | ICDM Workshops |
Keywords | Field | DocType |
data owner,randomized kernels,data privacy,pattern classification,matrix algebra,external cloud computing service,randomized kernel matrix,quadratic computational complexity,computational complexity,nonlinearly separable data,outsourcing,privacy preserving kernel k-means outsourcing,normal kernel k-means algorithm,limited computing resource,cloud computing,cloud computing service providers,kernel k-means,privacy-preserving kernel,kernel k-means problem,proposed randomized kernel k-means | Kernel (linear algebra),Data mining,Computer science,Outsourcing,Theoretical computer science,Tree kernel,Polynomial kernel,Information privacy,Kernel method,Cluster analysis,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-4799-3143-9 | 4 |
PageRank | References | Authors |
0.39 | 16 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keng-Pei Lin | 1 | 117 | 11.61 |