Attribute Value Weighting in K-Modes Clustering
<|reference_start|>Attribute Value Weighting in K-Modes Clustering: In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.<|reference_end|>
@article{he2007attribute,
    title={Attribute Value Weighting in K-Modes Clustering},
    author={Zengyou He, Xaiofei Xu, Shengchun Deng},
    journal={arXiv preprint arXiv:cs/0701013},
    year={2007},
    number={Tr-06-0615},
    archivePrefix={arXiv},
    eprint={cs/0701013},
    primaryClass={cs.AI}
}