The feature extraction and dimension reduction research based on weighted FCM clustering algorithm

The high dimensional data brings dimensionality disaster problem in information fusion and pattern recognition, the similarity frequency band between feature information is clustered by the improved fuzzy clustering algorithm, the feature extraction and data dimension reduction are realized in feature distributed band respectively. The threshold and clustering parametre are established by compactness degree and separation degree between samples, the cluster validity criterion function is given to determine the optimal clustering for samples, and finally an illustration verifies this method.