Abstract: Aiming at the problem that the traditional K-means clustering algorithm is sensitive to the initial value and fall into local extreme points easily, resulting in unsatisfactory data classification results, this paper proposes a hesitant fuzzy Canopy-K-means clustering algorithm. Firstly, the original data is preliminarily classified by the Canopy algorithm, and a set of Canopy centers with overlapping data is formed, that is, the initial cluster center of the K-means algorithm is obtained. Then, the K-means clustering algorithm is used for clustering to obtain the final clustering result. Finally, based on the evaluation information data of enterprises that resumed work and production after the epidemic, an example analysis is carried out, and 5 enterprises that have resumed work and production are analyzed from 6 aspects to evaluate the enterprises’ business development. The new proposed algorithm and the traditional K-means clustering algorithm are compared and analyzed, and the results show that the new proposed method greatly reduces the number of iterations, and the clustering results are more reasonable, stable and effective.

Key words: hesitant fuzzy set, clustering analysis, K-means clustering, Canopy algorithm