Research On Methods Of Complex Simulation Data Dimension Reduction And Visualization Clustering

The simulation systems are more and more complicated with the development ofscience and technology. The simulation data also appears high-volume,high-dimension,and many uncertain characteristics such as randomness, artificiality and so on. The dataanalysis applying classical statistical theories reveals a series of problems. With thedevelopment of computer hardware technology and data mining, complex simulation dataanalysis based on data mining technology gradually enters the sight of researchers. Thispaper studies visual clustering and related issues of the simulation data with large-scale,high-dimension, and the complex relationship. It is based on visual data miningtechnology. The research has a certain theoretical and engineering value.It often has to select feature before the complex simulation data visualization. Theexpert personal differences and data characteristics may be ignored in the traditionalexpert estimate method, the subjective and objective estimation method based on fuzzycomprehensive evaluation model is proposed because. First, the expert fuzzy evaluationmatrix is constructed, and expert right weights are determined according to the expertsinfluence in the industry. It is the subjective fuzzy comprehensive evaluation. Then theattribute information entropy is calculated according to the data nature features. Finally,the subjective evaluation and objective information entropy are integrated in differentproportions, thus the degree of importance of the attributes are determined.One of the important issues is the dimension reduction for visualization data miningof complex simulation data. First the main manifold learning dimension reductionmethod are analyzed in detail; Then both local tangent space alignment algorithm (LTSA)and kernel principal component analysis (KPCA) are deduced and proved consistencyessentially from mathematics; Finally an improved LTSA algorithm based on kernel forthe incremental simulation data is proposed. The experiments confirm the improvedLTSA algorithm achieve the same effects for dimension reduction as the LTSA algorithm,and the former has a higher efficiency than the latter.The dimension reduction of complex simulation data needs give the intrinsic dimension in advance. Aiming at this problem, an improved maximum likelihoodestimation of the intrinsic dimension estimation is suggested in this paper. Theshortcomings of the maximum likelihood method are analyzed, geodesic distance is used,instead of Euclidean distance, to solve the nearest neighbor selection errors; In order toavoid influencing the estimation result too much by the singular value, the average ofevery local intrinsic dimension is replaced by density correction value.Two novel methods are proposed in the study of the visualization clustering of complexsimulation data. In the visualization clustering method based on the improved radar chart,the traditional radar chart is improved to highlight the characteristics of the data, whichattribute weights determine polar angles, attribute values determine polar radius. k-meansalgorithm randomly select initial centers and can not get the optimal solution, and themethod of optimized initial centers is given. And the algorithm needs to be given thenumber of clusters in advance, but it is actually very difficult, an improved method usingof cyclic and expert supervision is put forward. In the visualization clustering methodbased on self-organizing map(SOM), nerve in the traditional rectangular or hexagonalgrid element mapping is changed into the radar chart mapping to solve the traditionalSOM can not reflect the real disparities between the data points; The algorithm accelerateconvergence by increasing the lateral contraction force and then reconstructing theweighted vector; proposed to winning neuron to the neighborhood neurons pitch relatedmonotonically decreasing function as a correction value of the adaptive learning speedimprovement, to increase the stability of the algorithm and convergence ti Two novelmethods are proposed in the study of the visualization clustering of complex simulationdata. In the visualization clustering method based on the improved radar chart, thetraditional radar chart is improved to highlight the characteristics of the data, whichattribute weights determine polar angles, attribute values determine polar radius. k-meansalgorithm randomly select initial centers and can not get the optimal solution, and themethod of optimized initial centers is given. And the algorithm needs to be given thenumber of clusters in advance, but it is actually very difficult, an improved method usingof cyclic and expert supervision is put forward. In the visualization clustering methodbased on self-organizing map(SOM), nerve in the traditional rectangular or hexagonal grid element mapping is changed into the radar chart mapping to solve the traditionalSOM can not reflect the real disparities between the data points; The algorithm accelerateconvergence by increasing the lateral contraction force and reconstructing the weightedvector; The monotonically decreasing function with the distances between winningneuron to neighborhood neuron was induced to adaptively correct the learning speed, andthen to increase the stability and accelerate convergence. The experiments prove that thealgorithm has higher efficiency and robustness.The paper enriches dimension reduction of high-dimensional data, visualization datamining method, and provides the technical support for complex simulation data analysismethods.