Ensemble clustering method based on Bagging

A Bagging-based ensemble methods using a new data sampling technology to keep the diversity and correlation of sub-sample is proposed,and then component learner is generated by using an improved K-means algorithm,the different clustering results of dataset is deal with according to mutual information,finally the distance between disputable object and the clustering center is computed and them is put to new clustering.The experiments on UCI machine learning benchmark data sets show that this method better improve the clustering performance.