中南大学学报（自然科学版）2017，Vol.48Issue(10)：2715-2722,8.DOI:10.11817/j.issn.1672-7207.2017.10.022
基于点过程模拟的时空级联模式统计挖掘方法
A statistical approach for discovering spatio-temporal cascading patterns based on point process simulation
摘要
Abstract
The discovery of spatio-temporal cascading patterns was modeled as a significance test for prevalence of candidate patterns under the null model of independence, and a statistical approach based on point process simulation was proposed. Firstly, null model was constructed by simulating the point process of different features. Then, the empirical distribution of prevalence of each candidate pattern was estimated by using Monte Carlo simulation. Lastly, significant spatio-temporal cascading patterns were identified by performing the significant test for the observed prevalence. The results show that the approach can effectively detect the meaningful spatio-temporal cascading patterns without threshold of prevalence measure.关键词
时空数据挖掘/时空级联模式/时空点过程/显著性检验Key words
spatio-temporal data mining/spatio-temporal cascading patterns/spatio-temporal point process/significance test分类
天文与地球科学引用本文复制引用
徐枫,陈袁芳,蔡建南,刘启亮,邓敏..基于点过程模拟的时空级联模式统计挖掘方法[J].中南大学学报（自然科学版）,2017,48(10):2715-2722,8.基金项目
国家自然科学基金资助项目(41471385) （41471385）
湖南省自然科学杰出青年基金资助项目(14JJ1007)(Project(41471385) supported by the National Natural Science Foundation of China （14JJ1007）
Project(14JJ1007) supported by the Science Foundation for Distinguished Young Scholars of Hunan Province) （14JJ1007）
