Abstract
Previous approaches to solve the trajectory outlier detection problem exclusively examine single outliers. However, anomalies in trajectory data may often occur in groups. This paper introduces a new problem, group trajectory outlier detection (GTOD) and proposes a novel algorithm, named, CD\(k\text {NN}\)-GTOD (Closed DBSCAN kNearest Neighbors for Group Trajectory Outlier Detection). The process starts by determining micro clusters using the DBSCAN algorithm. Next, a pruning strategy using \(k\text {NN}\) is performed for each micro cluster. Finally, an efficient pattern mining algorithm is applied to the resulting subsets of group of trajectory candidates to determine the group of trajectory outliers. We performed a comparative study using real trajectory databases to evaluate the proposed approach. The results have shown the efficiency and effectiveness of CD\(k\text {NN}\)-GTOD.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Belhadi, A., Djenouri, Y., Lin, J.C.W.: Comparative study on trajectory outlier detection algorithms. In: 2019 International Conference on Data Mining Workshops (ICDMW), pp. 415–423. IEEE (2019)
Chalapathy, R., Toth, E., Chawla, S.: Group anomaly detection using deep generative models. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 173–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_11
Das, K., Schneider, J., Neill, D.B.: Anomaly pattern detection in categorical datasets. In: Proceedings of the 14th ACM SIGKDD, pp. 169–176 (2008)
Djenouri, Y., Belhadi, A., Lin, J.C.W., Djenouri, D., Cano, A.: A survey on urban traffic anomalies detection algorithms. IEEE Access 7, 12192–12205 (2019)
Djenouri, Y., Djenouri, D., Lin, J.C.W., Belhadi, A.: Frequent itemset mining in big data with effective single scan algorithms. IEEE Access 6, 68013–68026 (2018)
Djenouri, Y., Lin, J.C.W., Nørvåg, K., Ramampiaro, H.: Highly efficient pattern mining based on transaction decomposition. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1646–1649. IEEE (2019)
Li, J., Zhang, J., Pang, N., Qin, X.: Weighted outlier detection of high-dimensional categorical data using feature grouping. IEEE Trans. Syst. Man Cybern. Syst. 99, 1–14 (2018)
Pei, J., Han, J., Mao, R., et al.: CLOSET: an efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, vol. 4, pp. 21–30 (2000)
Soleimani, H., Miller, D.J.: ATD: anomalous topic discovery in high dimensional discrete data. IEEE Trans. Knowl. Data Eng. 28(9), 2267–2280 (2016)
Toth, E., Chawla, S.: Group deviation detection methods: a survey. ACM Comput. Surv. (CSUR) 51(4), 77 (2018)
Xiong, L., Póczos, B., Schneider, J., Connolly, A., VanderPlas, J.: Hierarchical probabilistic models for group anomaly detection. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 789–797 (2011)
Xiong, L., Póczos, B., Schneider, J.G.: Group anomaly detection using flexible genre models. In: Advances in Neural Information Processing Systems, pp. 1071–1079 (2011)
Zhang, D., Li, N., Zhou, Z.H., Chen, C., Sun, L., Li, S.: iBAT: detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 99–108 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Djenouri, Y., Nørvåg, K., Ramampiaro, H., Li, J.CW. (2020). Fast and Accurate Group Outlier Detection for Trajectory Data. In: Darmont, J., Novikov, B., Wrembel, R. (eds) New Trends in Databases and Information Systems. ADBIS 2020. Communications in Computer and Information Science, vol 1259. Springer, Cham. https://doi.org/10.1007/978-3-030-54623-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-54623-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-54622-9
Online ISBN: 978-3-030-54623-6
eBook Packages: Computer ScienceComputer Science (R0)