C-means clustering algorithm based on intuitionistic fuzzy sets and its application in satisfaction evaluation Academic Article uri icon


  • 2018, Ubiquitous International. All rights reserved. Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. The partition matrix is initialized by given conditions, and the cluster center matrix is obtained through the iterative computation between the object matrix and the partition matrix. The final results are achieved according to the membership degrees and non-membership degrees of the objects to the partition matrix. Several important parameters during the intuitionistic fuzzy C-means clustering process, such as the initial form of the partition matrix, the number of classification and the threshold of terminating the iteration, which significantly affect the clustering results, are analyzed and discussed. Finally, a case of customer satisfaction evaluation is illustrated by the intuitionistic fuzzy C-means clustering method, and the method is compared with the fuzzy C-means clustering method as well.

published proceedings

  • Journal of Information Hiding and Multimedia Signal Processing

author list (cited authors)

  • Zhou, X. G., Lu, M., & Huang, X. X.

complete list of authors

  • Zhou, XG||Lu, M||Huang, XX

publication date

  • March 2018