Abstract: In order to deal with the problem that traditional spectral clustering algorithms need set parameters manually, this paper proposes a feature selection algorithm based on adaptive neighborhood mutual information and spectral clustering, which takes the advantage of neighborhood rough sets to deal with continuous data. First, the standard deviation set and adaptive neighborhood set of each object on attribute are defined. Some uncertainty measures such as adaptive neighborhood entropy, average neighborhood entropy, joint entropy, neighborhood conditional entropy and neighborhood mutual information are given, and then the adaptive neighborhood mutual information is used to sort the correlation between features and labels. Second, the shared nearest neighbor spectral clustering algorithm is combined to cluster the strongly relevant features into the same feature cluster, so that the features in the different feature clusters are strongly diverse. Finally, the feature selection algorithm is designed by employing the minimum redundancy and maximum correlation technology. The experimental results of selecting the number of features and classification accuracy on ten datasets verify the effectiveness of the proposed algorithm.

Key words: feature selection, neighborhood rough set, adaptive neighborhood mutual information, spectral clustering, minimum redundancy and maximum correlation