## 6 Clustering Results and Analysis  
In order to further analyze the driver's steering characteristics under the steering conditions after clustering, the results are shown by the average angular velocity and the average steering angle. As shown in Figures 11-13, the fuzzy C-means algorithm, the Gustafson-Kessel algorithm and the Gath-Geva algorithm were used to divide the driver's steering test results into four classes, and in each algorithm, the cluster center for each class is marked. (0.18, 0.59), (0.33, 0.18), (0.55, 0.785) and (0.785, 0.625) are the four clustering centers given by the fuzzy C-means algorithm; the Gustafson-Kessel algorithm gives four clustering centers: (0.155, 0.62), (0.27, 0.305), (0.46, 0.53) and (0.76, 0.67). The four cluster centers (0.115, 0.63), (0.255, 0.365), (0.44, 0.48) and (0.742, 0.649) are given by the Gath-Geva algorithm. According to the fourth cluster center, we can see that the average angular velocity and the average steering angle of the drivers' steering are larger. This reflects the driver's steering intention under the U-turn condition. Analysis of the first and second cluster centers revealed that these two types of conditions exist; the lower average angular velocity and larger average steering angle. It can be judged at this time that the first cluster reflects the drivers' steering intention under the lane change condition, and the second cluster centers reflects drivers' steering intention under the lane keeping condition. The remaining third cluster center between the fourth and first two cluster centers usually belongs to the ordinary driver's intentions for the turn right/left steering conditions. Figure 11: Visualization of the classification results of the driver’s steering intention based on the fuzzy C-means algorithm. Three kinds of clustering analysis methods can be used to separate the experimental data under different steering conditions into four different types. However, by comparing the three clustering methods, we can find that for the third cluster center, the average steering angle is greater than the average steering angle of the center of the fourth cluster using the fuzzy C-means algorithm, which is contrary to the fact that the average steering angle under the normal right/left turn is less than the average steering angle of the U-turn. Therefore, the accuracy of the Gustafson-Kessel algorithm and the Gath-Geva algorithm is superior to the fuzzy C-means algorithm. In order to analyze the clustering effect more scientifically, in the clustering method, the most representative criteria for evaluating the clustering effect are the Partition Coefficient (_PC_) and the Classification Entropy (_CE_), which are defined as follows:  
Figure 12: Visualization of the classification results of the driver’s steering intention based on the Gustafson–Kessel algorithm. Figure 13: Visualization of the classification results of the driver’s steering intention based on the Gath–Geva algorithm. The Partition Coefficient (\(PC\)) measures the amount of \"overlapping\" between clusters. It is defined by [PERSON] as follows:  
\[PC\ (c)=\frac{1}{N}\sum_{i=1}^{c}\sum_{j=1}^{N}\left(\mu_{ij}\right)^{2} \tag{16}\]  
The Classification Entropy (\(CE\)) measures the fuzziness of the cluster partition only, which is similar to the partition coefficient. \[CE\ (c)=-\frac{1}{N}\sum_{i=1}^{c}\sum_{j=1}^{N}\mu_{ij}\log(\mu_{ij}) \tag{17}\]  
In this paper, \(C\) is the number of clusters and \(N\) is the total number of experiments. When the two validity evaluation functions reach the optimal value, that is \(PC\ (c)\) reaches the maximum value and \(CE\ (c)\) reaches the minimum value, the clustering analysis effect is better. The comparisons of \(PC\) (\(c\)) and \(CE\ (c)\) and the required time among different clustering algorithms under different working conditions are shown in Table 5.  
By analyzing Table 6, we can see that the Gath-Geva algorithm achieves the maximum value for the Partition Coefficient (\(PC\)).