Research On The Clustering Analysis Based On Ant Colony Algorithm And Rough Sets

Graph clustering analysis is one of hot researches in the image processing field. It has been involved into many application fields of the national economy. the application of the national economy in many fields Nowadays, a great deal of the researches have been done by many scholars. Because the application of cluster analysis in image pattern recognition plays an important role in the unity of image and recognition theory system, therefore, the study of graph clustering analysis theory is of great significance.This article mainly includes the several aspects as follows:Firstly, the thesis gives a brief introduction to the theories and methods of rough and ant colony optimization algorithm, which include information expression system, upper approximation and lower approximation, attribute reduction and core, attribute dependency. and the concepts of importance and the summary about the ant colony algorithm knowledge, etc.Secondly, the image segmentation and feature extraction method has been systematically summarized. Image segmentation and measurement is the basis clustering analysis. The image is segmented with some recognized regions by the image segmentation. how to select the threshold is the key of image segmentation. In addition, the methods of clustering analysis have been studied and in the article.Finally, this paper puts forward a clustering analysis method based on rough set theory and optimization colony algorithm. The method makes full use of the optimal capacity of ant colony algorithm to realize the image recognition. First of all, whole of samples are Numbered and then extract digital features, and digital clustered with ant colony algorithm., to evaluate the clustering results based on the positive domain of rough set theory. evaluating clustering results. The experimental results show results show that the proposed method is effective and feasible.