Improved K-means algorithm and its implementation based on density
Chen Zhou · Journal of Computer Applications · 2011
The initial clustering center of the traditional K-means algorithm was generated randomly from the data set,and the clustering result was unstable.An improved K-means algorithm based on density algorithm optimizing initial clustering center was proposed,which selected the furthest mutual distance k points in high-density region as the initial centers.The experimental results demonstrate that the improved K-means algorithm can eliminate the dependence on the initial cluster center,and the clustering result has been greatly improved.