Mining Co-locations from Continuously Distributed Uncertain Spatial Data

摘要：

　　A co-location pattern is a group of spatial features whose instances tend to locate together in geographic space.While traditional co-location mining focuses on discovering co-location patterns from deterministic spatial data sets,in this paper,we study the problem in the context of continuously distributed uncertain data.In particular,we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances are represented as multivariate Gaussian distributions.We first formulate the problem of probabilistic co-location mining based on newly defined prevalence measures.When the locations of instances are represented as continuous variables,the major challenges of probabilistic co-location mining lie in the efficient computation of prevalence measures and the verification of the probabilistic neighborhood relationship between instances.We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges.Our experiments on multiple datasets demonstrate the effectiveness of the proposed algorithm.

作者: Bozhong Liu Ling Chen Chunyang Liu Chengqi Zhang Weidong Qiu

作者单位: School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,C Centre for Quantum Computation and Intelligent Systems,University of Technology Sydney,Sydney,Austra School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,C

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 66-78

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）