The Research On Fuzzy C-Means Cluster Analysis And Its Applications

As an important branch of unsupervised pattern recognition, fuzzy c-means cluster has wide application in the fields of pattern recognition, data mining, computer vision, fuzzy control and other fields. Fuzzy C-means (FCM) cluster algorithm is an algorithm based on objective function. It has the advantages of deep mathematical theoretical foundation, simple design as well as widely range of solving problems. Meanwhile lots of FCM algorithms basing on other prototype have been formed. However there still were many problems to be solved, such as need to define the parameters of cluster prototype artificially, cluster results easily falling into local peak, long time consuming under large volume data and unable to process the data of special types directly. So the FCM algorithm needs further improvements.On the basis of numerous research achievements, this paper does comparatively deep research on the existent insufficiency of FCM cluster algorithm. Based on the theories of bootstrapping, statistical characteristics of samples and kernel function, it proposed two kinds of modified FCM cluster algorithms respectively. It also put forward a new validity index to evaluate the partition results according to Shannon entropy and fuzzy variation theory. In addition, by considering the effect of neighbor pixels and designing weight, the paper presented a weighted FCM algorithm that could be used in the segmentation on noisy images. And for some more complex images, we initialized the number of cluster center by calculating the validation function. Finally we combined FCM algorithm with neural network and used them in the design of vehicle classification. Experiments proved that the modified algorithms were feasible and its validity function had good classification property.