Aiming at the problem that traditional Fuzzy C-Means (FCM) clustering algorithm is easily affected by noise in processing noise images, a noise image segmentation method of wavelet domain feature enhancement based on FCM was proposed. Firstly, the noise image was decomposed by two-dimensional wavelet. Secondly, the approximate coefficient was enhanced at the edge, and Artificial Bee Colony (ABC) optimization algorithm was used to perform threshold processing to the detail coefficients, and then the wavelet reconstruction was carried out for the processed coefficients. Finally, the reconstructed image was segmented by FCM algorithm. Five typical grayscale images were selected, and were added with Gaussian noise and salt-and-pepper noise respectively. Various methods were used to segment them, and the Peak Signal-to-Noise Ratio (PSNR) and Misclassification Error (ME) of the segmented images were taken as performance indicators. Experimental results show that the PSNR of the images segmented by the proposed method is at most 281% and 54% higher than the PSNR of the images segmented by the traditional FCM clustering algorithm segmentation method and Particle Swarm Optimization (PSO) segmentation method respectively, and the segmented images of the proposed method has the ME at most 55% and 41% lower than those of the comparison methods respectively. It can be seen that the proposed segmentation method preserves the edge texture information well, and the anti-noise and segmentation performance of this method are improved.

Bank card payments account for a large proportion in the social consumption, which plays a major role in the promotion of economic growth. So, predicting consumer behavior is important. However, the traditional methods are difficult to effectively deal with complex data and dynamic changes. Based on this, a customer behavior prediction method for card consumption based on two-step clustering and Hidden Markov Chain (HMC) was presented. Firstly, consumer behaviors were conduced by pattern clustering based on sequence; then the secondary clustering was conducted by introducing penalty clustering, which carried out the equilibrium division of the hierarchical states in the sequential pattern. Secondly, HMC was used to estimate the state transition of consumption levels in the sequence and predict the future consumer behavior of the users. Finally, the experimental comparison and analysis results on the traditional clustering, clustering without penalty and clustering with penalty show that the proposed method based on two-step clustering and HMC is more suitable to the consumer behavior prediction model.