Abstract
Discovering various types of frequent patterns in spatiotemporal data is gaining attention of researchers nowadays. We consider spatiotemporal data represented in the form of events, each associated with location, type and occurrence time. The problem is to discover all significant sequential patterns denoting spatial and temporal relations between event types. In the paper, we adapted a microclustering approach and use it to effectively and efficiently discover sequential patterns and to reduce size of dataset of instances. Appropriate indexing structure has been proposed and notions already defined in the literature have been reformulated. We modify algorithms already defined in literature and propose an algorithm called Micro-ST-Miner for discovering sequential patterns in event-based spatiotemporal data.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994). http://dl.acm.org/citation.cfm?id=645920.672836
Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.S.: Scalable sweeping-based spatial join. In: Proceedings of the 24rd International Conference on Very Large Data Bases, VLDB ’98, pp. 570–581. Morgan Kaufmann Publishers Inc., San Francisco (1998). http://dl.acm.org/citation.cfm?id=645924.671340
Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. 41(3), 111–125 (2008). http://www.sciencedirect.com/science/article/pii/S092577210700106X
Boinski, P., Zakrzewicz, M.: Algorithms for spatial collocation pattern mining in a limited memory environment: a summary of results. J. Intell. Inf. Syst. 43(1), 147–182 (2014). https://doi.org/10.1007/s10844-014-0311-x
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD ’00, pp. 1–12. ACM, New York (2000). https://doi.org/10.1145/342009.335372
Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)
Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. Proc. VLDB Endow. 3(1–2), 723–734 (2010). https://doi.org/10.14778/1920841.1920934
Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pp. 1099–1108. ACM, New York (2010). https://doi.org/10.1145/1835804.1835942
Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pp. 236–245. ACM, New York (2004). https://doi.org/10.1145/1014052.1014080
Mohan, P., Shekhar, S., Shine, J.A., Rogers, J.P.: Cascading spatio-temporal pattern discovery. IEEE Trans. Knowl. Data Eng. 24(11), 1977–1992 (2012)
Shekhar, S., Evans, M.R., Kang, J.M., Mohan, P.: Identifying patterns in spatial information: a survey of methods. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(3), 193–214 (2011). https://doi.org/10.1002/widm.25
Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements, pp. 1–17. Springer, Berlin (1996). https://doi.org/10.1007/BFb0014140
Thabtah, F.: A review of associative classification mining. Knowl. Eng. Rev. 22(1), 37–65 (2007). https://doi.org/10.1017/S0269888907001026
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, KDD’97, pp. 283–286. AAAI Press (1997). http://dl.acm.org/citation.cfm?id=3001392.3001454
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001). https://doi.org/10.1023/A:1007652502315
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD ’96, pp. 103–114. ACM, New York (1996). https://doi.org/10.1145/233269.233324
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Macia̧g, P.S. (2019). Efficient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_14
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