A new approach for noise data detection based on cluster and information entropy

To detect the noise data in the datasets and remove them, a new approach for noise data detection based on fast search and find density peaks (FSFDP) and information entropy (IE) was proposed in this article. In the proposed method, FSFDP was used to cluster the original datasets and remove the outliers. Then construct the rectangular panes and mesh generation for each class according to the clustering results. Calculate the IE of each class after projecting all samples to the mesh, and remove the samples which have the lower local density in the class. If the IE value change obviously after the sample was removed from the class, the sample was marked as a noise. Finally, the result of the experiment shows that the presented approach is effective and accurately.