An Improved k-means Algorithm for Documents Clustering
Xiaojun Wan · Jisuanji gongcheng · 2003
This paper first introduces the partitioning-based k-means algorithms for documents clustering. The k-means algorithm adapts to processing the vast amount of documents, but it is sensitive to outliers. So this paper puts forward an idea to separate the clustering centroid from the clustering seed and brings forward an algorithm based on this idea to improve the k-means algorithm. The paper shows the results of the experiments to prove that this algorithm is more veracious and stable than the k-means algorighm.