Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method

@inproceedings{Li2004TowardsMD,
  title={Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method},
  author={Dan Li and Jitender S. Deogun and William Spaulding and Bill Shuart},
  booktitle={Rough Sets and Current Trends in Computing},
  year={2004}
}
In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm. 
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