期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2013
卷号：4
期号：6
页码：1043-1046
出版社：Technopark Publications
摘要：With the maturity and wide accessibility of GPS, wireless, telecommunication, and internet technologies, massive amounts of object movement information are collected from varied moving objects like animals, mobile devices, vehicles, and climate radars. Analyzing such information has deep implications in several applications, e.g., traffic control. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. we propose R-tree that's spatial data structure to mine the spatial co-location patterns. For mine the patterns necessary spatial information is transformed into the compact format. Here, we've got adapted the R-tree structure that converts the spatial information with its feature into the transactional format. Then, the prominent pattern mining rule, FP growth is engaged to mine the spatial co-location patterns from the converted format of data. Finally, the performance of the planned technique is compared with the previous technique in terms of time and memory usage. From the results, we are able to make sure that the planned technique outperformed of concerning over five hundredth of previous algorithmic rule in time and memory usage
关键词：Spatial Mining; Co-Location Patterns; Minimum Support; Minimum Bounding Rectangle; FP Tree; Vehicle Movement Data
