Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4951200 | Journal of Computer and System Sciences | 2017 | 29 Pages |
Abstract
As a typical representative of big data, geographic spatiotemporal big data present new features especially the non-stationary feature, bringing new challenges to mine correlation information. However, representation of instantaneous information is the main bottleneck for non-stationary data, but the traditional non-stationary analysis methods are limited by Heisenberg's uncertainty principle. Therefore, we firstly represent instantaneous frequency of geographic spatiotemporal big data based on Hilbert-Huang transform to overcome traditional methods' weakness. Secondly, we propose absolute entropy correlation analysis method based on KL divergence. Finally, we select five geographic factors to certify that the absolute entropy correlation analysis method is effective and distinguishable.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Weijing Song, Lizhe Wang, Yang Xiang, Albert Y. Zomaya,