Bridging Density Functional Theory and Big Data Analytics with Applications

• Activity Number: 131
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: International Chinese Statistical Association
• Author(s): Henry Lu* and Chien-Chang Chen and Hung-Hui Juan and Meng-Yuan Tsai
• Companies: National Chiao Tung University and NCTU and NCTU and NCTU
• Keywords: density functional theory ; big data ; image analysis

Abstract

The framework of the density functional theory (DFT) reveals strong suitability and compatibility for investigating large-scale systems in the Big Data regime. By technically mapping the data space into physically meaningful bases, the article provided a simple procedure to formulate global Lagrangian and Hamiltonian density functionals to circumvent the emerging challenges on large-scale data analyses. Then, the informative features of mixed datasets and the corresponding clustering morphologies can be visually elucidated by means of the evaluations of global density functionals. Barriers of Lagrangian density formed between data groups in the morphology indicate the threshold of data mixtures and the strength of data affinity, whereas the density trenches depict the enclosures of data groups. By carefully considering these extracted features, the evolution of data migration under diverse circumstances can be also visually illustrated. Simulation results and empirical studies illustrate that the proposed methodology provides an alternative route for analyzing the data characteristics with insights.