An Improved K-means Algorithm
Yu Zhang · Jisuanji gongcheng · 2003
Clustering analysis plays an important role in scientific research and commercial application. Kmeans algorithm is a widely used partition method in clustering. As the datasets scale increases rapidly, it is difficult to use Kmeans to deal with massive data. An improved Kmeans algorithm is presented, which can avoid getting into locally optimal solution in some degree, and reduce the probability of dividing a big cluster into two or more ones owing to the adoption of Jc. The experiments demonstrate the improved Kmeans is more stable and more accurate.