期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2014
卷号：9
期号：8
页码：406-413
DOI：10.14445/22312803/IJCTT-V9P173
出版社：Seventh Sense Research Group
摘要：In day to day life, vehicles have become important aspects in human life where each vehicle is manufactured for a particular purpose. Colocation pattern discovery is intended towards the processing data with spatial perspectives to determine classes of spatial objects that are frequently located together. Mining colocation patterns from spatial databases may disclose the types of spatial features which are likely located as neighbours in space. In the existing system they use FPtree to mine the spatial data. In this paper, I have presented an algorithm for mining spatially colocated moving objects using spatial data mining techniques. I propose a novel algorithm for colocation pattern mining which is used to identify the vehicles movements behaviour with the aid of Cluster based Temporal Mobile Sequential Pattern Mine Algorithm. Location Based Service alignment helps in finding the similarities between vehicles. An approach for Time segmentation is provided to find the time intervals where similar vehicle characteristics exist. In the experimental evaluation the proposed mining technique produces better results in spatial vehicle moving datasets.
关键词：Spatial data mining; Co-location patterns; Frequent Pattern Tree; Location Based Service; CTMSPMine Algorithm; Vehicle movement data.
