xk-split:A Split Clustering Algorithm Bases on k-medoids

In recent years,the scale of internet data has explosive growth,which makes big data analysis become a hot topic.However,it is difficult to directly utilize the collected data,so a certain degree of pretreatment had to be made in order to improve the quality of big data.In this work,the data set will be gradually divided into smaller subsets by using the split iterative process,which can effectively avoid the limitation of traditional clustering algorithm and reduce the time complexity.In addition,by threshold-based noise data filtering,the dirty data can be eliminated during the iterative process so as to enhance the tolerance of the clustering algorithm to the dirty data.