Frontiers of Computer Science

, Volume 12, Issue 5, pp 1032–1034 | Cite as

Center-based clustering of categorical data using kernel smoothing methods

  • Xuanhui Yan
  • Lifei Chen
  • Gongde Guo


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This work was supported by the National Natural Science Foundation of China (Grant No. 61672157), and the Innovative Research Team of Probability and Statistics: Theory and Application (IRTL1704).

Supplementary material

11704_2018_7186_MOESM1_ESM.ppt (178 kb)
Supplementary material, approximately 177 KB.


  1. 1.
    Jain A K, Murty M N, Flynn P J. Data clustering: a review. ACM Computing Survey, 1999, 31(3): 264–323CrossRefGoogle Scholar
  2. 2.
    Jing L, Ng M K, Huang J Z. An entropy weighting K-means algorithm for subspace clustering of high-dimensinoal sparse data. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(8): 1–16CrossRefGoogle Scholar
  3. 3.
    Sun H, Wang S, Jiang Q. FCM-based model selection algorithms for determining the number of clusters. Pattern Recognition, 2004, 37(10): 2027–2037CrossRefzbMATHGoogle Scholar
  4. 4.
    Ouyang D, Li Q, Racine J. Cross-validation and the estimation of probability distributions with categorical data. Nonparametric Statistics, 2006, 18(1): 69–100MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Bai L, Liang J, Dang C, Cao F. A novel attribute weighting algorithm for clustering high-dimensional categorical data. Pattern Recognition, 2011, 44(12): 2843–2861CrossRefzbMATHGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and InformaticsFujian Normal UniversityFuzhouChina

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