Visual Information Fusion And Early Warning Plans Based In The Layed And Ordered Figs Models Of Multivariate Datas

Massive data processing in domestic and international problems and difficulties of a full analysis, this mass of data for the medicine, the main mass of data of high dimensional data hierarchy, hierarchical dimension reduction problem. Firstly it explains the high dimensional data reduction the background, importance and urgency. How to make high-dimensional data dimension reduction algorithm using a more appropriate medical treatment and analysis of massive data, this problem has been a hot topic in today's medical profession, but there are many domestic and foreign experts and scholars have done in this area out a welcome contribution. This paper is to study the issue of huge amounts of data preprocessing, the use of massive data preprocessing is appropriate, is the key to the success of biomarker identification. In this study, also made reference to many domestic and foreign research methods, pericoin the application of cluster analysis combined with genetic algorithms. Lilien principal component analysis and linear discriminant method. Morris the use of wavelet transform and peak detection algorithm. Yu using high-throughput mass spectrometry data algorithm development.This article focuses on the geometric properties of massive data, given the type of mass data, and dimensionality reduction and data features of the mathematical description of the concept. Secondly, the paper introduces a hierarchical graph-based dimensionality reduction and visual information fusion method to introduce a hierarchical graph models, information visualization, massive data reduction and fusion of visual information visualization information fusion method, and various methods were simple comparison shows the advantages and disadvantages of each method.