Existing clustering ensemble algorithm starts from the perspective of cluster members, if all the cluster members are used, the ensemble result is affected by the inferior members. If the cluster members are selected and then used in ensemble, the selected strategy has subjectivity. To avoid these two limitations to some extent, from the perspective of elements, this paper proposed a new clustering fusion method. It selected a part of class-determined elements through multi-granulation rough sets with incongruous decisions, and then used this part of the elements to generate a new clustering. Multi-granulation rough set model with incongruous decisions could describe the phenomenon of inconsistent decisions with consistent attribute set. Therefore, this paper proposed a model of multi-granulation rough set with incongruous decisions and a clustering ensemble algorithm based on the model. First of all, it ran a K-means clustering algorithm several times on the data set in the case and generated multiple granule structures. Next, it calculated inclusion degrees among all the granulations, and obtained the matrix of inclusion degree. Used Otsu's method to generate a threshold, then got several group of granulation that met the threshold condition. According to the model of multi-granulation rough set with incongruous decision, it obtained lower and upper approximations. Finally, classified the elements of lower approximation and boundary separately to obtained a clustering that has been fused. The experiments show that the algorithm has a high time efficiency and robustness, which improves the result of K-means clustering.

[2019-10-05] Printed Article /doc/2018.04.0217/v2