GeT_Move: An Efficient and Unifying Spatio-temporal Pattern Mining Algorithm for Moving Objects
Recent improvements in positioning technology have led to a massive moving object data. A crucial task is to find the moving objects that travel together. Usually, they are called spatio-temporal patterns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to the fact that it is a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. Additionally, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine spatio-temporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT_Move, using a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT_Move is implemented in two versions which are GeT_Move and Incremental GeT_Move. Experiments are performed on real and synthetic datasets and the results show that our approaches are very effective and outperform existing algorithms in terms of efficiency.
KeywordsSpatio-temporal pattern frequent closed itemset trajectories
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- 1.Gudmundsson, J., van Kreveld, M.: Computing Longest Duration Flocks in Trajectory Data. In: GIS 2006, New York, NY, USA, pp. 35–42 (2006)Google Scholar
- 2.Wang, Y., Lim, E.-P., Hwang, S.-Y.: Efficient Mining of Group Patterns from User Movement Data. In: DKE 2006, pp. 240–282 (2006)Google Scholar
- 3.Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of Convoys in Trajectory Databases. In: PVLDB 2008, vol. 1(1), pp. 1068–1080 (2008)Google Scholar
- 5.Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: KDD 1996, Portland, pp. 226–231 (1996)Google Scholar
- 6.Li, Z., Ding, B., Han, J., Kays, R.: Swarm: Mining Relaxed Temporal Moving Object Clusters. In: VLDB 2010, Singapore, pp. 723–734 (2010)Google Scholar
- 7.Romero, A.O.C.: Mining Moving Flock Patterns in Large Spatio-Temporal Datasets Using a Frequent Pattern Mining Approach. Master Thesis, University of Twente (March 2011)Google Scholar
- 8.Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets. In: ICDM FIMI (2004)Google Scholar
- 9.Jensen, C.S., Lin, D., Ooi, B.C.: Continuous Clustering of Moving Objects. In: KDE, pp. 1161-1174 (2007) ISSN: 1041-4347Google Scholar
- 10.Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W.: Mining, Indexing, and Querying Historical Spatiotemporal Data. In: SIGKDD 2004, pp.236-245 (2004)Google Scholar