Approximation Algorithms for K-Modes Clustering
<|reference_start|>Approximation Algorithms for K-Modes Clustering: In this paper, we study clustering with respect to the k-modes objective function, a natural formulation of clustering for categorical data. One of the main contributions of this paper is to establish the connection between k-modes and k-median, i.e., the optimum of k-median is at most twice the optimum of k-modes for the same categorical data clustering problem. Based on this observation, we derive a deterministic algorithm that achieves an approximation factor of 2. Furthermore, we prove that the distance measure in k-modes defines a metric. Hence, we are able to extend existing approximation algorithms for metric k-median to k-modes. Empirical results verify the superiority of our method.<|reference_end|>
@article{he2006approximation,
    title={Approximation Algorithms for K-Modes Clustering},
    author={Zengyou He},
    journal={arXiv preprint arXiv:cs/0603120},
    year={2006},
    number={Tr-06-0330},
    archivePrefix={arXiv},
    eprint={cs/0603120},
    primaryClass={cs.AI}
}