Aiming at the problems that it is difficult to search the optimal neighborhood radius through manual debug-ging in most neighborhood systems,and that traditional K-means clustering requires random selection of cluster centers and the number of specified clusters,this paper proposed a feature selection method using neighborhood mutual inform-ation and feature clustering with K-means.Firstly,the average distance of the sample from other samples under each feature is taken as the adaptive neighborhood radius,and the neighborhood set of the sample is determined.Then to re-flect the correlation between features,some metrics are presented,such as adaptive neighborhood entropy,neighbor-hood mutual information,normalized neighborhood mutual information,etc.Secondly,an adaptive K neighbor set is constructed based on the normalized neighborhood mutual information,and the weighted K neighbor density is defined by using the feature weight with the Pearson correlation coefficient so that the K-means algorithm can automatically se-lect the cluster center.The K-means feature clustering is completed well.Finally,the weighted average redundancy de-gree is given,and the feature with the largest weighted average redundancy in each feature cluster is selected to form the optimal subset of features.Experimental results show that the developed algorithm can not only effectively improve the classification results of feature selection,but also obtain better clustering effects.
维普
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