K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset
<|reference_start|>K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset: Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-histogram, a new efficient algorithm for clustering categorical data. The k-histogram algorithm extends the k-means algorithm to categorical domain by replacing the means of clusters with histograms, and dynamically updates histograms in the clustering process. Experimental results on real datasets show that k-histogram algorithm can produce better clustering results than k-modes algorithm, the one related with our work most closely.<|reference_end|>
@article{he2005k-histograms:,
    title={K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset},
    author={Zengyou He, Xiaofei Xu, Shengchun Deng, Bin Dong},
    journal={arXiv preprint arXiv:cs/0509033},
    year={2005},
    number={Tr-2003-08},
    archivePrefix={arXiv},
    eprint={cs/0509033},
    primaryClass={cs.AI}
}