Work

Clustering with diversity

Year: 2010

Type: preprint

Abstract: We consider the {\em clustering with diversity} problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least $\ell$ points, all of which have distinct colors. We give a 2-approximation to this problem for any $\ell$ when the objective is to minimize the maximum radius of any cluster. We show that the approximation ratio is optimal unless $\mathbf{P=NP}$, by providing a matching lower bound. Several extensions to our algorithm have also been developed for handling outliers. This problem is mainly motivated by applications in privacy-preserving data publication.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1004.2968

Authors: Li, Jian, Yi, Ke, Zhang, Qin

Institutions: University of Maryland, College Park, Hong Kong University of Science and Technology

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2231768786

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