A novel method for discovering spatio-temporal clusters of different sizes, shapes, and densities in the presence of noise  

作　　者：Qiliang Liu Min Deng Jiantao Bi Wentao Yang 

机构地区：[1]Department of Surveying and Geo-informatics,Central South University,Changsha,China [2]Department of Land Surveying and Geo-Informatics,Hong Kong Polytechnic University,Hong Kong [3]Center for Earth Observation and Digital Earth,Chinese Academy of Sciences,Beijing,China

出　　处：《International Journal of Digital Earth》2014年第2期138-157,共20页国际数字地球学报（英文）

基　　金：The work described was supported by the Major State Basic Research Development Program of China(973 Program),No.2012CB719906;Program for New Century Excellent Talents in University(NCET),No.NCET-10-0831;National Natural Science Foundation of China(NSFC),No.40871180.

摘　　要：The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery.In this paper,a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors(STSNN)is proposed to detect spatio-temporal clusters of different sizes,shapes,and densities in spatiotemporal databases with a large amount of noise.The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatiotemporal density for a spatio-temporal entity with definite mathematical meanings.Then,the density-based clustering strategy is employed to uncover spatio-temporal clusters.The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities.Experiments are undertaken on several simulated datasets to demonstrate the effectiveness and advantage of the STSNN algorithm.Also,the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.

关 键 词：spatio-temporal clustering shared nearest neighbor windowed distance spatio-temporal density foreshocks and aftershocks data mining Digital Earth geo-spatial science geospatial data integration 

分 类 号：P31[天文地球—固体地球物理学]