2015/03/02 by Xing He, Qian Ai, He, Xing +12
Computer Science · Decision Sciences · Mathematics · #Computational Physics and Python Applications #Data Visualization and Analytics #FOS: Computer and information sciences #Methodology (stat.ME) #Simulation Techniques and Applications #stat.ME
paper · pdf · doi:10.48550/arxiv.1503.00463
5 pages, 7 figures, submitted to PESGM 2015. arXiv admin note: substantial text overlap with arXiv:1502.00060
arxiv created 2015/03/02 · openalex publication_date 2015/03/02 · arxiv updated 2015/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data with features of volume, velocity, variety, and veracity are challenging traditional tools to extract useful analysis for decision-making. By integrating high-dimensional analysis with visualization, this paper develops a 3D power-map animation as an effective solution to the challenge. An architecture design, with detailed data processing procedure, is proposed to realize the integration. Two of the most important components in the architecture are presented: the Single-Ring Law for random matrices as solid mathematic foundation, and the proposed statistical index MSR as high-dimensional data for visualization. The whole procedure is easy in logic, fast in speed, objective and even robust against bad data. Moreover, it is an unsupervised machine learning mechanism directly oriented to the raw data rather than logics or models based on simplifications and assumptions. A case study validates the effectiveness and performance of the developed 3D power-map in analysis extraction.