Abstract: To deal with noise reduction of kernel fuzzy spectral clustering for obtaining a better cluster capability, a new spectral clustering based on local outlier factor was presented. With the proposed method the distribution features of outliers were analyzed. Based on the analysis result, the cluster center candidate object was proposed by using local filter factor algorithm. And then a process was constructed to filter the noise data and highlighted normal data's influence in the clustering center adjustment. Secondly, the local filter factor was presented by using local outlier factor between two arbitrary objects. Then the local filter factor was used as weighting factor to improve the similarity measure of kernel fuzzy spectral clustering. The improved similarity measure made weight between normal data and normal data became bigger and weight between normal data and noise data smaller. Therefore, the improved kernel fuzzy spectral clustering can reduce greatly the sensitivity of outliers. The validity experiment and stability experiment results show the proposed method has better clustering accuracy and robustness.

Key words: spectral clustering, kernel fuzzy spectral clustering, cluster center candidate object, local filter factor