Adaptive Affinity Propagation Clustering Algorithm With Applications

As the saying goes:"people in groups, feather flock together. " Cluster analysis is the technology using of computer for this purpose. It consists of two basic elements: pattern similarity measure and clustering algorithm. The input is a set of unclassified data, do not know in advance how to classify or may not know in several classes, but by statistical analysis of the relationship between data, formulate a reasonable clustering rules, a reasonable division, to determine the class of each data, and finally according to the similarity to some of the data gathered into clusters. To ensure similarity of data within the cluster greater, the similarity between clusters of data smaller.Frey and Dueck 2007, presents a new clustering method, called "affinity propagation clustering" (Affinity Propagation, AP). Affinity propagation clustering (compared with the K-Means cluster) do not need to pre-designated number of clusters and initial cluster centers, and the final cluster centers must be the exact original data exist in the data points, not by the number of data point obtained by averaging the cluster center (K-Means). The test proved that the use of its data clustering can be small errors and so on. The algorithm has been used in the current face image retrieval, exon discovery, the optimal route search and so on.Affinity propagation clustering method compared to other clustering has many advantages, and in practical application has made a certain effect, but the algorithm is still in its infancy, there are still some key and unresolved issues, particularly in the following areas:1) Affinity propagation clustering can not be predicted number of clusters before the final cluster, and clustering results can not be guaranteed that the optimal clustering results; 2) Affinity propagation clustering is an unsupervised clustering method that cannot complete the semi-supervised learning, which has been marked by a small amount of samples to guide clustering process; 3) Time complexity and space complexity of Affinity propagation clustering severely limited by the number of sample data,and it can not handle large-scale data such as image segmentation.This article started on these problems in discussion of analysis and research, and tries to combine a number of other current technologies (such as:semi-supervised learning theory, adaptive clustering technology, etc.) to solve the existing problems of the algorithm. In this paper, do the following work:(1) The cluster analysis and classification system of the narrative, at home and abroad on the cluster analysis methods and applications are briefly introduced.(2) Depth study of the ideological affinity propagation clustering algorithm, clustering processes and applications, and describes the pro-clustering algorithm and the dissemination of the current situation and existing problems and challenges.(3) Details of the current clustering evaluation functions of several major, including external evaluation, internal evaluation, the relative evaluation method, and described the evaluation of various methods of representative characteristics and optimizing the role of the division, summed up the clustering Application of evaluation methods.(4) Affinity propagation clustering algorithm for optimal clustering results is difficult, we propose a semi-supervised clustering algorithm for adaptive transmission affinity (SAAP). It can be combined with a small amount of labeled samples from the relationship between the performance parameters and the number of cluster, to achieve the effective number of clusters adaptively scanning room, and finally find the optimal clustering based on the results of the evaluation function.It can solve the existing low precision, slow operation, the final number of clusters does not match with the real situation and other shortcomings.(5) For affinity propagation clustering algorithm is not suitable for large-scale data processing, particularly image segmentation problem, we propose a large-scale color image segmentation method based on affinity propagation. First, the original image color space conversion, and then the data sampling, the sampling data for the given number of clusters of affinity propagation clustering (APGNC), then the result will be extended to clustering the entire image, and finally combined with morphology . of poly Class results were merging, the segmentation results have been fixed. It can resolve that Affinity propagation clustering is difficult to deal with large-scale image segmentation and the poor segmentation.