Research Based On Multi_Map Graphics Primitives And Classification Of Multi_Dimensional

Pattern recognition in multi-dimensional data classification is an importa- ant research topic.So far ,the classification algorithms exist the following issues :traditional classification algorithms for compute-intensive problem that requir- es an object classification and recognition of the increasingly complex issues, classification results may be an explanatory difference,and the unknown of cl- assified process.To address the above problems,this paper study how to reduce compution cost and Realize visualization classification by the method of multi-map graphics primitives,the characteristics of multi-primitve and the feature extraction technolgy.Visualization classification general method of multi-dimension data is proposed based on multi-map graphics primitives and the characteristics of multi-primitve.Firstly,after thoroughly study the principle of multi-map graphics, multi-map graphics charcteristicis is excavated.In view of dimension between 3~15 data,visuallization classification method is proposed based on the principle of multi-map graphic unioning varable fusion,which uses the total dimensional data.Fistly.Different multi-dimension data formes different multi-map graphics and distinguished different category.And then,a shortest distance mean classifier is structured to implement automatic recognize multi-map graphics.The experimental results prove that it has better classified precision compared to the traditional classification algorithm.Secondly,regarding the dimensions between 15~30 data(middle-high dimension),in order to achieve automatic classification of multi-map graphics,we must study the multi-map graphic description of methods and equipment suitable for distinguishing characteristics.So this paper proposes the visualizaion classification method which applied Feature extraction to recognize the multi-map graphics.then realize the feature reduction,In order not to miss the data information,the other data use the features fusion based on vectors composite method then find a standard template to classfy.Finally,in view of multi- dimensional data of more than 30 dimensions through multi-dimensional data decomposition,the method of hierarchical and multi-layer has been proposed which is used to classification of higher dimension data .this method is extended to more dimensions of data classification,expanded the scope of application of this method.Finally the experimental results prove that it has realized visualization classification with the higher classified precision.