K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data
<|reference_start|>K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data: Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular k-means algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, Average Normalized Mutual Information-ANMI) borrowed from cluster ensemble. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-art categorical data clustering algorithms with respect to clustering accuracy.<|reference_end|>
@article{he2005k-anmi:,
    title={K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical
  Data},
    author={Zengyou He, Xiaofei Xu, Shengchun Deng},
    journal={arXiv preprint arXiv:cs/0511013},
    year={2005},
    number={Tr-2004-03},
    archivePrefix={arXiv},
    eprint={cs/0511013},
    primaryClass={cs.AI cs.DB}
}