## 4 Discussion
### Fuzzy Clustering  
The optimal number of clusters is affected by the initial value, the number of iterations and the initial value selection is random. Within the limited number of iterations, the compactness, separation, and Xie-Beni index values will have a slight deviation but the trend remains unchanged. Before clustering, a reasonable range of initial values should be given to accelerate the convergence speed and improve the algorithm's accuracy. The global significance of the local regression FCM algorithm can be seen from the clustering results. From Figure 6, according to the amplitude of wind speed fluctuation, the wind speed set in Figure 6 can be divided into five categories, in which the serial data numbers are 1: 180, 181: 208, 209: 301, 302: 442 and 443: 500, respectively. The five data types fluctuate around the three clustering centers of 4.979, 2.497, and 0.998, and the degree of discretization among the data is significant. According to the calculation results of the algorithm and combined with the regulation means of underground ventilation facilities, managers can investigate and evaluate the hidden dangers in the ventilation system.  
Determining the mine ventilation state according to the clustering number benefits the global deduction of the monitoring data. The wind speed value has significant time variation characteristics. The wind network structure, system components (roadways, air doors, wind windows, wind bridges, main fans, local fans, etc.), and characteristic parameters (air volume, air resistance, atmospheric state parameters, working air volume, and wind pressure of main fans, etc.) are not fixed values, but dynamic. The complete air volume data should make the monitoring air volume consistent with the empirical value and follow the law of air volume distribution. Although the on-site technicians can accurately grasp the air volume of some branches and have a general understanding of the air volume of most of the other branches, it is not easy to control the air volume distribution pattern of the whole ventilation network. There are two kinds of methods to obtain the target air volume of the ventilation network. The first method is to determine the air volume of the roadway comprehensively and the second method is to select part of the air volume data as the test standard. The method of comprehensive measurement of roadway air volume has the following disadvantages: (1) large workload; (2) when the ventilation system is abnormal, not only the characteristics of the components are difficult to obtain, but also the amount of data obtained is limited; (3) there are errors between the obtained data, which do not fully meet the node equation. It is challenging to eliminate errors artificially. When selecting some branches to preprocess the wind speed data and carry on the cluster analysis, according to the calculation results, comparing the air volume changes of each branch according to the air volume distribution law, we can roughly calculate the air volume changes of the remaining branches.  
Compared with the fuzzy clustering methods in the image and medical field, the clustering validity function is introduced in this paper, which solves the problem of cluster number certainty very well [33; 34]. The data dimension in the field of mine ventilation is single but it implies the relevant information of ventilation state, therefore it is necessary to analyze the interpretability of clustering. Clustering based on a fuzzy decision tree combines the flexibility of fuzzy division with the interpretability of the decision tree, which can be added to wind speed clustering in subsequent research [35].