Abstract
Nowadays, electronic devices such as mobile phones, sensors embedded in vehicles, and more generally digital acquisition devices continuously provide information relating the state of the surrounding environment at nearly real-time. These data provide important information required for real-time systems monitoring such as temperature regulation in smart building, traffic planning to relieve network congestion in smart cities, etc. The growing demand for analysing such data encourages researchers to adopt an approach known as “streaming analysis” which aims at processing data streams at real-time to continuously capture data evolution over time. Data stream clustering approaches already exist but require keeping in memory all the input data and have a slow adaptation to data changes. To solve these problems, we propose to agentify the clusters and allow them to fuse and evolve locally and autonomously. This work presents AMAS4DC a generic Dynamic Clustering model based on Adaptive Multi-Agent System approach. AMAS4DC processes acquired data on the fly using local similarity evaluation for cluster’s creation or fusion. AMAS4DC is then instantiated on two use cases: the dynamic clustering of itineraries to detect regularities and the dynamic clustering of traffic data to predict future traffic. The conducted experiments underline the performance of AMAS4DC in terms of memory usage, processing time and clustering quality compared to well-known models for dynamic clustering.
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Notes
- 1.
Package clusopt core. https://pypi.org/project/clusopt-core/, accessed: 2021-04-16.
References
Aggarwal, C.C., Philip, S.Y., Han, J., Wang, J.: A framework for clustering evolving data streams. In: Proceedings 2003 VLDB Conference, pp. 81–92. Elsevier (2003)
Ahmed, M., Seraj, R., Islam, S.M.S.: The \(k\)-\(means\) algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020)
Bouguelia, M.R., Belaïd, Y., Belaïd, A.: An adaptive incremental clustering method based on the growing neural gas algorithm. In: ICPRAM (2013)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3, 1–27 (1974)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: SDM (2006)
Chaimontree, S., Atkinson, K., Coenen, F.: A multi-agent based approach to clustering: harnessing the power of agents. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) ADMI 2011. LNCS (LNAI), vol. 7103, pp. 16–29. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27609-5_3
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (1996)
Fritzke, B.: A growing neural gas network learns topologies. In: Proceedings of the 7th International Conference on Neural Information Processing Systems, pp. 625–632. MIT Press (1994)
Ghesmoune, M., Lebbah, M., Azzag, H.: Clustering over data streams based on growing neural gas. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 134–145. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_11
Ghesmoune, M., Lebbah, M., Azzag, H.: State-of-the-art on clustering data streams. Big Data Anal. 1, 13 (2016). https://doi.org/10.1186/s41044-016-0011-3
Grachev, S., Skobelev, P., Mayorov, I., Simonova, E.: Adaptive clustering through multi-agent technology: development and perspectives. Mathematics 8(10), 1664 (2020)
Hensher, D.A., Ho, C.Q., Mulley, C., Nelson, J.D., Smith, G., Wong, Y.Z.: Understanding Mobility as a Service (MaaS): Past, Present and Future. Elsevier, Amsterdam (2020)
Isaksson, C., Dunham, M.H., Hahsler, M.: SOStream: self organizing density-based clustering over data stream. In: Perner, P. (ed.) MLDM 2012. LNCS (LNAI), vol. 7376, pp. 264–278. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31537-4_21
Lehmann, A.L., Alvares, L.O., Bogorny, V.: SMSM: a similarity measure for trajectory stops and moves. Int. J. Geogr. Inf. Sci. 33(9), 1847–1872 (2019)
Ngo, H.N., Kaddoum, E., Gleizes, M.P., Bonnet, J., Anaïs, G.: Life-long learning system of driving behaviors from vehicle data streams. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 1132–1139 (2021)
Palacio-Niño, J.O., Berzal, F.: Evaluation metrics for unsupervised learning algorithms. arXiv preprint arXiv:1905.05667 (2019)
Perles, A., Crasnier, F., Georgé, J.-P.: AMAK - a framework for developing robust and open adaptive multi-agent systems. In: Bajo, J., et al. (eds.) PAAMS 2018. CCIS, vol. 887, pp. 468–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94779-2_40
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Rundensteiner, E.A., Ding, L., Zhu, Y., Sutherland, T., Pielech, B.: CAPE: a constraint-aware adaptive stream processing engine. In: Chaudhry, N.A., Shaw, K., Abdelguerfi, M. (eds.) Stream Data Management. ADBS, vol. 30, pp. 83–111. Springer, Boston (2005). https://doi.org/10.1007/0-387-25229-0_5
Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data Models and Simulation. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32460-4
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 791–800 (2009)
Acknowledgements
These works were carried out within the VILAGIL MaaS action with the support of the French Government as part of the Territoire d’Innovation program, an initiative of the Grand Plan d’Investissement linked to France 2030, Toulouse Métropole, and the GIS neOCampus. Also, we would like to thank Continental Digital Services France for supporting these works.
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Perles, A., Ngo, H.N., Kaddoum, E., Camps, V. (2024). Adaptive Multi-agent System for Dynamic Clustering Applied to Itineraries Regularities and Traffic Prediction. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_5
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