文献类型：期刊文献

英文题名：Research of mining algorithms for uncertain spatio-temporal co-occurrence pattern

作者：Wang, Zhanquan[1]; Lu, Bowen[1]; Ying, Fangli[1]; Kong, Man[1]; Tang, Minwei[1]

机构：[1] Academy of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2017

起止页码：12

外文期刊名：2017 9th International Conference on Knowledge and Smart Technology: Crunching Information of Everything, KST 2017

收录：EI(收录号：20171603584565)

语种：英文

外文关键词：Filtration

摘要：Spatial Co-location pattern mining and Spatio-temporal Co-occurrence pattern mining are important directions of spatial data mining. However, the existing relevant mining algorithms are computational expensive and the algorithms can't effectively deal with uncertain data which are wide spread in many areas. The fast co-occurrence data mining algorithms for the uncertain data are proposed by using the filter-refine method and efficient pruning strategy. The correctness, completeness and complexity of the proposed algorithms are analyzed, and the experimental data shows that the algorithms are effective and reasonable. ? 2017 IEEE.