ABSTRACT
Trajectory pattern mining has been performed over many datasets, including animal movement, GPS trajectories, and human travel history. This paper aims to explore and mine individual frequently visited regions and trajectory patterns using online footprints captured through a social media site (i.e., Twitter). Regions of frequent visits representing daily activity areas at which an individual appears are derived using the DBSCAN clustering algorithm. A trajectory pattern mining algorithm is then applied to discover ordered sequences of these spatial regions that the individual visits frequently. To illustrate and test the effectiveness of the proposed methods, we analyze the activity patterns of a selected Twitter user using the geo-tagged tweets posted by the user for an extended period. The preliminary assessment indicates that our approach can be applied to mine individual frequent trajectory patterns from online footprints that are of relatively low and irregular spatial and temporal resolutions.
- Agrawal, R., and Srikant, R. 1995. Mining sequential patterns. In Data Engineering, 1995. Proceedings of the Eleventh International Conference on (pp. 3--14). IEEE. Google ScholarDigital Library
- Ashbrook, D., and Starner, T. 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5), 275--286. Google ScholarDigital Library
- Birant, D., and Kut, A. 2007. ST-DBSCAN: An algorithm for clustering spatial---temporal data. Data & Knowledge Engineering, 60(1), 208--221. Google ScholarDigital Library
- Eagle, N., and Pentland, A. 2006. Reality mining: sensing complex social systems. Personal and ubiquitous computing, 10(4), 255--268. Google ScholarDigital Library
- Ester, M., Kriegel, H. P., Sander, J., and Xu, X. 1996. August). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD. 96, 226--231. Google ScholarDigital Library
- Frank, A, Raper, J, and Cheylan, J. P. 2001. Life and motion of spatial socio-economic units. Taylor & Francis, London.Google Scholar
- Gidófalvi, G., and Pedersen, T. B. 2007. Cab-sharing: An effective, door-to-door, on-demand transportation service. In Proceedings of the 6th European Congress on Intelligent Transport Systems and Services (2007).Google Scholar
- Giannotti, F., Nanni, M., Pinelli, F., and Pedreschi, D. 2007. Trajectory pattern mining. In KDD, pages 330--339. Google ScholarDigital Library
- Giannotti F., M. Nanni, D. Pedreschi, and F. Pinelli. 2009. Trajectory pattern analysis for urban traffic. In GISIWCTS, pages 43--47, 2009. Google ScholarDigital Library
- Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., and Hsu, M. C. 2000. FreeSpan: frequent pattern-projected sequential pattern mining. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 355--359). ACM. Google ScholarDigital Library
- Huang, Q., and Xu, C. 2014. A Data-Driven Framework for Archiving and Exploring Social Media Data. Annals of GIS, 20 (4), 265--277.Google ScholarCross Ref
- Huang, Q., Cao, G., and Wang, C. 2014. From Where Do Tweets Originate? - A GIS Approach for User Location Inference. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN '14), 4--7 Novermber Dallas, TX, USA. New York: ACM, 1--8. Google ScholarDigital Library
- Huang, Q., and Wong, D., 2016. Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographic Information Science, 30(9): 1873--1898. Google ScholarDigital Library
- Iwase, S, and Saito, H. 2002. Tracking soccer player using multiple views. In Proceedings of the IAPR Workshop on Machine Vision Applications.Google Scholar
- Jeung, H., Yiu, M. L., and Jensen, C. S. 2011. Trajectory pattern mining. In Computing with spatial trajectories. Springer, New York, 143--177.Google Scholar
- Lee, J.-G., Han, J., and Whang, K.-Y. 2007. Trajectory clustering: a partition-and-group framework. In SIGMOD Conference, 593--604. Google ScholarDigital Library
- Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., and Cheung, D. W. 2004. Mining, indexing, and querying historical spatiotemporal data. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, 236--245. Google ScholarDigital Library
- Mennis, J., and Guo, D. 2009. Spatial data mining and geographic knowledge discovery-An introduction. Computers. Environment and Urban Systems, 33(6), 403--408.Google ScholarCross Ref
- Monreale, A., Pinelli, F., Trasarti, R. and Giannotti, F., 2009. Wherenext: a location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 30 June -- 01 July Paris, France. New York: ACM, 637--646. Google ScholarDigital Library
- Morzy, M. 2006. Prediction of moving object location based on frequent trajectories. In Computer and Information Sciences---ISCIS 2006. Springer, Berlin, Heidelberg, 583--592). Google ScholarDigital Library
- Morzy, M. 2007. Mining frequent trajectories of moving objects for location prediction. In Machine Learning and Data Mining in Pattern Recognition Springer, Berlin Heidelberg, 667--680. Google ScholarDigital Library
- Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M. C. 2001. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE Computer Society, 0215--0215. Google ScholarDigital Library
- Sumpter, N., Bulpitt, A. 2000. Learning spatio-temporal patterns for predicting object behaviour. Image Vision Computing, 18, 697--704.Google ScholarCross Ref
- Slimani, T., and Lazzez, A. 2013. Sequential Mining: Patterns and Algorithms Analysis. International Journal of Computer and Electronics Research, 2(5), 639--647.Google Scholar
- Yang, J., and Hu, M. 2006. Trajpattern: Mining sequential patterns from imprecise trajectories of mobile objects. In Advances in Database Technology-Edbt 2006, Springer, Berlin Heidelberg, 664--681. Google ScholarDigital Library
- Zheng Y., L. Zhang, X. Xie, and W.-Y. Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In WWW, 2009, 791--800. Google ScholarDigital Library
- Zheng V. W., Y. Zheng, X. Xie, and Q. Yang. 2010. Collaborative location and activity recommendations with gps history data. In WWW, 2010, 1029--1038. Google ScholarDigital Library
- Zhou, C., Shekhar, S., and Terveen, L. 2005. Discovering personal paths from sparse gps traces. In 1st International Workshop on Data Mining in conjunction with 8th Joint Conference on Information Sciences.Google Scholar
Index Terms
- Mining frequent trajectory patterns from online footprints
Recommendations
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the ...
Identification of adverse disease agents and risk analysis using frequent pattern mining
Highlights- An improved algorithm is proposed to construct FP-tree from transactional datasets.
AbstractLife-threatening illnesses such as cancer, cirrhosis of the liver, and hepatitis have become crucial problems for humanity. The risk of mortality can be deflated by early detection of symptoms and providing the best possible diagnosis. ...
Mining inter-sequence patterns
Sequential pattern and inter-transaction pattern mining have long been important issues in data mining research. The former finds sequential patterns without considering the relationships between transactions in databases, while the latter finds inter-...
Comments