Research of Text Clustering Based on Fuzzy Granular Computing
基于模糊粒度计算的K-means文本聚类算法研究

The traditional K-means is very sensitive to initial clustering centers and the clustering result will wave with the different initial inpul To remove this sensitivity,a new method was proposed to get initial clustering centers.This method is as follows:provide a normalized distance function in the fuzzy granularity space of data objects,then use the function to do a initial clustering work to these data objects who has a less distance than granularity dλ,then get the initial clustering centers.The test shows this method has such advantages on increasing the rate of accuracy and reducing the program times.