Mining of Spatially Co-Located Moving Objects by Using CTMSPMINE

1. ABSTRACT In day to day life, vehicles have become important aspects in human life where each vehicle is manufactured for a particular purpose. Co-location pattern discovery is intended towards the processing data with spatial perspectives to determine classes of spatial objects that are frequently located together. Mining co-location 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 co-located moving objects using spatial data mining techniques. I propose a novel algorithm for co-location 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.