Fuzzy Cluster Analysis And Its Application In The Digital Image Processing

Fuzzy C-means clustering algorithm (FCM) is an important method of unsupervised pattern classification, and has an important position in pattern recognition. But in real life, there are a lot of known knowledge, and a large number of samples have known information, how to take full advantage of the known information of cluster become a hotspot of research.In this paper, first of all, we re-elaborate the issue of the semi-supervised clustering, and describe the known knowledge by class, and discusse the research status of the semi-supervised clustering. Based on the feature weighted FCM algorithm, the known knowledge is added to the optimal problem, we get a new feature weighted FCM algorithm with semi-supervision. The problem can be solved by HPR algorithm, but HPR algorithm is suitable for the general nonlinear optimal question with constraint conditions, and is added a lot of middle variables, so make the computational complexity higher. In view of this, throng appropriate variable substitution and the thought of HPR algorithm, we obtain the new algorithm to solve the feature weighting FCM algorithm with semi-supervision. Compared with the original FCM algorithm, the number of variables of the new algorithm is not increased, thus there is little effect on its speed of operation. IRIS data experiment shows that the new algorithm not only deepens the discussion scope of the semi-supervised FCM algorithm, but also makes the computation complexity little. Compared with the existing semi-supervised FCM algorithm, the new algorithm has greater improvement, and provides a way of thinking for the FCM algorithm with supervision.Image segmentation is an essential element in image processing, but also an important imaging technology. Through experiments we found that the feature weighted FCM algorithm with semi-supervision can achieve better results in image segmentation; handwritten numeral recognition is the more successful one research topic in the field of image processing and pattern recognition. Through experiments found that the feature weighted FCM with semi-supervised can obtain a better recognition effect in handwritten numeral recognition.