A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Singularity analysis of ore-mineral and toxic trace elements in stream sediments
Computers & Geosciences
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Least squares quantization in PCM
IEEE Transactions on Information Theory
Regression modeling in back-propagation and projection pursuit learning
IEEE Transactions on Neural Networks
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A new hybrid exploratory data analysis method, fractal projection pursuit classification (FPPC) model, is developed on the basis of the projection pursuit (PP) and fractal models. In this model, objective classification results are obtained by applying the projection index on the basis of the number-size fractal model. The real-coded acceleration genetic algorithm (RAGA) is used to optimize the projection index to establish the optimum projection direction in the model. Stream sedimentary geochemical data, Gejiu Mining District, Yunnan Province, China, were chosen in a case study to demonstrate the processing data analysis using FPPC. The results show that the anomalies are associated with known mineral deposits in the eastern part of the Gejiu District, and correlated with faults and granite in the western part of the study area. It is demonstrated that FPPC can be a powerful tool for multi-factor classification analysis and provide an effective approach to identify anomalies for mineral exploration.