Abstract
Spatial-temporal analysis is at the heart of data mining in Big Data Era. Most mathematical tools are incompetent to deal with spatial-temporal data. This phenomenon has greatly spurred the development of data science, especially in the field of BDA (big data analytics). This chapter proposes random matrix theory (RMT) to handle this problem, which begins by modeling spatial-temporal datasets as sequences, whose term is in the form of a random matrix each. Then, some fundamental RMT principles are briefly discussed, such as asymptotic spectrum laws, transforms, convergence rate, and free probability, in order to extract high-dimensional statistics from the random matrix as the indicators. The statistical properties of these indicators are discussed for a better understanding of the system. Finally, some potential application fields are given.
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