Analysis And Realization In Rainfall Based On Cluster Analysis And Association Rules

With the rapid development of the digital image processing technology, this makes it as an inevitable trend to dig out the information of satelite cloud images (SCI) and from the deep graduation and multi-angle. And the routine aplication of meteorological satellite data is undergoing a profound revolution.The developing trend of the Data Mining is diverse, which the flexibility of the technic of the data mining is known clearly.Sinee the improvement of the era of the information, more technic is widely interinfiltrated which involved the data mining.In this paper, the technic of the data mining is used in the field of the image proeessing.Lots of the researehing work has been done, which combine the technic of the data mining to the technic of the image proeessing, to solve the problem in remote sense image proeessing.In detail, the content and achievement of this paper are listed as follows:(1) Image feature extration. Various feature extraction algorithms are presented for satellite cloud images, which are, namely, the first and second order momentum, fractal number and grayscale co-existing matrixes, etc. The probability of adopting information entropies to describe the denseness-scale, an important notion in the analyses of SCIs, is discussed in the histogram statistics method. And the situation of qualitative analysis of denseness-scale for a long time is broken in this thesis.It is concluded that information entropies have excellent detecting effects to the texture features within the cloud clusters.(2) The clustering analysis of satellite nephogram. This paper is about the research of K-means.At first, some related concepts of clustering are given.The chief point of the paper is the research on K-means. K-means is a partitioning algorithm that constructs a partition of a database of n objects into a set of K clusters where K is an input parameter.Clustering use an iterative procedure,if this algorithm converges to one of numerous local minima,it terminates and outputs result. It proposes an improved algorithm K-means, based on little comparability between different clusters and large comparability in the same cluster, to set number K and the initial centroids.(3) Mining of association regulation based on nephograms. A great number of satellite nephograms and abundant record of rain quantity present original material for mining of association regulation. The problem is greatness of image data.Apriori algorithm, a classical algorithm, don't meet the need of efficiency.In order to deal with the problem, a new concept of transaction is introduced.Base on the concept, Apriori algorithm can be improved so to meet the need of efficiency.The aim of the project is to expiore new theories and methods for the analysis and mining of satellite cloud images and a brand-new approach for precipitation forecast.It has a broad foreground both in application and research.