2010 22nd IEEE International Conference on Tools with Artificial Intelligence (2010)
Oct. 27, 2010 to Oct. 29, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2010.44
The increasing availability of huge amounts of data pertaining to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory clustering, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a complete framework starting from data preparation task that allows us to make the mining step quite effective. Since the validation of data mining approaches has to be experimental we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.
Non Separable Transforms, Clustering
E. Masciari, "Lifting Trajectories for Effective Clustering," 2010 22nd IEEE International Conference on Tools with Artificial Intelligence(ICTAI), Arras, France, 2010, pp. 256-259.