Data stream clustering based on feature selection

Clustering in the data stream,the redundant features will affect the quality of data clustering,removing redundant features to improve the clustering quality is very important.To solve this problem,it is proposed that a data stream clustering algorithm based on feature selection(DSCFC).It is one-pass clustering algorithms,these are applied that ranking feature,grading feature,detecting redundant features and removing the redundant features algorithm and so on.The experimental results indicated that DSCFC algorithm can detect hidden redundant features in data stream and remove redundant features;when there are redundant features in the data stream clustering,the algorithm is more efficient than CluStream,clustering quality is better.