Abstract: Semi-supervised clustering is a new research direction of machine learning in recent years and an important branch of data mining,which has gradually become an useful tool in many areas.However,in the research for semi-supervised clustering now,especially when the number of classes in labled informations less than the entire data set,its clustering accuracy is not good.On the basis of the existing semi-clustering technology,the similarity of the samecluster of documents is improved by feature weighting with better clustering result.In order to verify the validity of this idea,experiment is carried out not only on the single-language data sets,but also the Sino-British data set in the labled document containing only Chinese or English language.The experimental results show that the method performs well.

Key words: parts of labled information, feature weighting, multi-language, semi-supervised clustering