对于给定的聚类块数七，不同的初始聚类中心很可能会导致截然不同的聚类结果

For a given number of clusters of seven, different initial cluster centers are likely to lead to completely different clustering results. Therefore, selecting an appropriate initial clustering center point will effectively improve the clustering accuracy of this type of algorithm, and the number of iterations of the algorithm will also be greatly reduced. That is to say, an excellent initial cluster center point selection algorithm can not only reduce the running time of the algorithm, but also significantly improve the clustering effect of the algorithm.

For a given number of cluster blocks seven, different initial clustering centers are likely to lead to different clustering results. Therefore, selecting the appropriate initial clustering center point will effectively improve the clustering accuracy of this kind of algorithm, and the number of iterations of the algorithm will be greatly reduced. In other words, an excellent initial clustering center point selection algorithm can not only reduce the running time of the algorithm, but also significantly improve the clustering effect of the algorithm

For a given number of clustering blocks of seven, different initial clustering centers will probably lead to completely different clustering results. Therefore, selecting an appropriate initial clustering center will effectively improve the clustering accuracy of this kind of algorithm, and the number of iterations of the algorithm will also be greatly reduced. In other words, an excellent initial clustering center selection algorithm can not only reduce the running time of the algorithm, but also significantly improve the clustering effect of the algorithm.