关键词: 文本聚类, K-means算法, 余弦相似度, 余弦距离, 初始点选取

Abstract: Text clustering is an important means for text information to be organized, abstracted and navigated effectively, in which K-means algorithm based on cosine similarity is one of the most widely used algorithms. Aiming at the problem that the K-means algorithm based on cosine similarity is difficult to be improved, and that many excellent K-means improvement methods based on Euclidean distance can not be applied, the relationship between cosine similarity and Euclidean distance is discussed, and the transformation formula of the two is obtained with standard vector. Thus, a definition of cosine distance is given, which is close to the Euclidean distance, so that the original improved K-means method based on Euclidean distance can be transformed into a cosine similarity K-means algorithm by cosine distance. On this basis, it is deduced the calculation method of cluster center points in cosine K-means algorithm, and the initial point selection  scheme is further improved to form a new text clustering algorithm MCSKM++. The experimental results show that the algorithm can improve the clustering accuracy while the number of iterations is reduced and the running time is shortened.

Key words: text clustering, K-means algorithm, cosine similarity, cosine distance, initial point selection