A fuzzy k-modes algorithm for clustering categorical data

@article{Huang1999AFK,
  title={A fuzzy k-modes algorithm for clustering categorical data},
  author={Joshua Zhexue Huang and Michael K. Ng},
  journal={IEEE Trans. Fuzzy Systems},
  year={1999},
  volume={7},
  pages={446-452}
}
This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of thek-means paradigm to efficiently cluster large categorical data sets. A fuzzyk-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results. 
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