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• W2175152600 abstract "We study the problem of r-anonymized clustering and give a k-means type extension. The problem is partition a set of objects into k different groups by minimizing the total cost between objects and cluster centers subject to a constraint that each cluster contains at least r objects. Previous work has reported an approach when the cluster centers are constrained to be a real member of the objects. In this paper, we release the constraint and allow a center to be the mean of the objects in its group, similar to the settings of the classical k-means clustering model. To address the inherent computational difficulty, we exploit linear program relaxation to find high quality solutions in an efficient manner. We conduct a series of experiments and confirm the effectiveness of the method as expected." @default.
• W2175152600 created "2016-06-24" @default.
• W2175152600 creator A5022112921 @default.
• W2175152600 date "2014-01-01" @default.
• W2175152600 modified "2023-10-02" @default.
• W2175152600 title "Privacy Preserving Clustering: A k-Means Type Extension" @default.
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• W2175152600 doi "https://doi.org/10.1007/978-3-319-12640-1_39" @default.
• W2175152600 hasPublicationYear "2014" @default.
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