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The real-world process of generating a large spatio-temporal data collection presents a very difficult technical problem. First, this process is very expensive, requiring a lot of various high-technology software tools and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; second, the recorded trajectories sometimes cannot represent any special traffic or movement...
As technology advances, detailed data on the position of moving objects, such as humans and vehicles is available. In order to discover groups of mobile objects that usually move in similar ways we propose an incremental clustering algorithm that clusters mobile objects according to similarity of their movement patterns. The proposed clustering algorithm uses a new, "data-amount-based" similarity...
As technology advances we encounter more available data on moving objects, which can be mined to our benefit. In order to efficiently mine this large amount of data we propose an enhanced segmentation algorithm for representing a periodic spatio-temporal trajectory, as a compact set of minimal bounding boxes (MBBs). We also introduce a new, "data-amount-based" similarity measure between...
Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site...
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