DBSCAN Speedup for Time-Serpentine Datasets

[1] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “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 (KDD-96), AAAI Press, 1996, pp. 226–231. [Online]. Available: https://file.biolab.si/papers/1996-DBSCANKDD.pdf Search in Google Scholar

[2] R. J. G. B. Campello, D. Moulavi, and J. Sander, “Density-based clustering based on hierarchical density estimates,” in: J. Pei, V. S. Tseng, L. Cao, and H. Motoda (eds.), Advances in Knowledge Discovery and Data Mining, vol. 7819. Springer Berlin Heidelberg, 2013, pp. 160–172. https://doi.org/10.1007/978-3-642-37456-2_14 Search in Google Scholar

[3] J. Sander, Generalized Density-Based Clustering for Spatial Data Mining. München, Herbert Utz Verlag, 1998. Search in Google Scholar

[4] V. V. Romanuke, “Speedup of the k-means algorithm for partitioning large datasets of flat points by a preliminary partition and selecting initial centroids,” Applied Computer Systems, vol. 28, no. 1, pp. 1–12, Jun. 2023. https://doi.org/10.2478/acss-2023-0001 Search in Google Scholar

[5] E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, “DBSCAN Revisited, revisited: Why and how you should (still) use DBSCAN,” ACM Transactions on Database Systems, vol. 42, no. 3, Jul. 2017, Art. no. 19. https://doi.org/10.1145/3068335 Search in Google Scholar

[6] N. Hanafi and H. Saadatfar, “A fast DBSCAN algorithm for big data based on efficient density calculation,” Expert Systems with Applications, vol. 203, Oct. 2022, Art. no. 117501. https://doi.org/10.1016/j.eswa.2022.117501 Search in Google Scholar

[7] J. Sander, M. Ester, H.-P. Kriegel, and X. Xu, “Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 169–194, Jun. 1998. https://doi.org/10.1023/A:1009745219419 Search in Google Scholar

[8] X. Huang, T. Ma, C. Liu, and S. Liu, “GriT-DBSCAN: A spatial clustering algorithm for very large databases,” Pattern Recognition, vol. 142, Oct. 2023, Art. no. 109658. https://doi.org/10.1016/j.patcog.2023.109658 Search in Google Scholar

[9] S. Pourbahrami, “A neighborhood-based robust clustering algorithm using Apollonius function kernel,” Expert Systems with Applications, vol. 248, Aug. 2024, Art. no. 123407. https://doi.org/10.1016/j.eswa.2024.123407 Search in Google Scholar

[10] J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A k-means clustering algorithm,” Journal of the Royal Statistical Society, Series C, vol. 28, no. 1, pp. 100–108, 1979. https://doi.org/10.2307/2346830 Search in Google Scholar

[11] A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Information Sciences, vol. 622, pp. 178–210, Apr. 2023. https://doi.org/10.1016/j.ins.2022.11.139 Search in Google Scholar

[12] Z. Wei, Y. Gao, X. Zhang, X. Li, and Z. Han, “Adaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm,” Expert Systems with Applications, vol. 238, Part E, Mar. 2024, Art. no. 122229. https://doi.org/10.1016/j.eswa.2023.122229 Search in Google Scholar

[13] J. Xie, L. Jiang, S. Xia, X. Xiang, and G. Wang, “An adaptive density clustering approach with multi-granularity fusion,” Information Fusion, vol. 106, 2024, Jun. Art. no. 102273. https://doi.org/10.1016/j.inffus.2024.102273 Search in Google Scholar

[14] B. Ma, C. Yang, A. Li, Y. Chi, and L. Chen, “A faster DBSCAN algorithm based on self-adaptive determination of parameters,” Procedia Computer Science, vol. 221, pp. 113–120, 2023. https://doi.org/10.1016/j.procs.2023.07.017 Search in Google Scholar

[15] Y. Chen, L. Zhou, N. Bouguila, C. Wang, Y. Chen, and J. Du, “BLOCK-DBSCAN: Fast clustering for large scale data,” Pattern Recognition, vol. 109, Jan. 2021, Art. no. 107624. https://doi.org/10.1016/j.patcog.2020.107624 Search in Google Scholar

[16] F. Ros, S. Guillaume, R. Riad, and M. El Hajji, “Detection of natural clusters via S-DBSCAN a self-tuning version of DBSCAN,” Knowledge-Based Systems, vol. 241, Apr. 2022, Art. no. 108288. https://doi.org/10.1016/j.knosys.2022.108288 Search in Google Scholar

[17] D. Luchi, A. L. Rodrigues, and F. M. Varejão, “Sampling approaches for applying DBSCAN to large datasets,” Pattern Recognition Letters, vol. 117, pp. 90–96, 2019. https://doi.org/10.1016/j.patrec.2018.12.010 Search in Google Scholar

[18] N. Newaliya and Y. Singh, “Multivariate hierarchical DBSCAN model for enhanced maritime data analytics,” Data & Knowledge Engineering, vol. 150, Mar. 2024, Art. no. 102282. https://doi.org/10.1016/j.datak.2024.102282 Search in Google Scholar

[19] P. Sadhukhan, L. Halder, and S. Palit, “Approximate DBSCAN on obfuscated data,” Journal of Information Security and Applications, vol. 80, Feb. 2024, Art. no. 103664. https://doi.org/10.1016/j.jisa.2023.103664 Search in Google Scholar

[20] J. Gan and Y. Tao, “DBSCAN revisited: Mis-claim, un-fixability, and approximation,” in Proceedings of the 2015 ACM SIGMOD International Conference on Manage ment of Data, May 2015, pp. 519 –530. https://doi.org/10.1145/2723372.2737792 Search in Google Scholar

[21] T. Boonchoo, X. Ao, Y. Liu, W. Zhao, F. Zhuang, and Q. He, “Grid-based DBSCAN: Indexing and inference,” Pattern Recognition, vol. 90, pp. 271–284, Jun. 2019. https://doi.org/10.1016/j.patcog.2019.01.034 Search in Google Scholar

[22] N. Gholizadeh, H. Saadatfar, and N. Hanafi, “K-DBSCAN: An improved DBSCAN algorithm for big data,” Journal of Supercomputing, vol. 77, no. 6, pp. 6214–6235, June 2021. https://doi.org/10.1007/s11227-020-03524-3 Search in Google Scholar

[23] M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander, “OPTICS: Ordering points to identify the clustering structure,” in ACM SIGMOD International Conference on Management of Data, ACM Press, Jun. 1999, pp. 49–60. https://doi.org/10.1145/304181.304187 Search in Google Scholar

[24] R. J. G. B. Campello, D. Moulavi, A. Zimek, and J. Sander, “A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies,” Data Mining and Knowledge Discovery, vol. 27, no. 3, pp. 344–371, Apr. 2013. https://doi.org/10.1007/s10618-013-0311-4 Search in Google Scholar

[25] M. Al Samara, I. Bennis, A. Abouaissa, and P. Lorenz, “Complete outlier detection and classification framework for WSNs based on OPTICS,” Journal of Network and Computer Applications, vol. 211, Feb. 2023, Art. no. 103563. https://doi.org/10.1016/j.jnca.2022.103563 Search in Google Scholar

[26] J. Wang, Z. Liu, Y. Zhao, Y. Xie, and Y. Xie, “EAST-NBI experimental data processing method based on improved OPTICS algorithm,” Fusion Engineering and Design, vol. 172, Nov. 2021, Art. no. 112737. https://doi.org/10.1016/j.fusengdes.2021.112737 Search in Google Scholar

[27] V. V. Romanuke, “Uniform rectangular array radar optimization for efficient and accurate estimation of target parameters,” Information and Telecommunication Sciences, vol. 13, no. 1, pp. 44–55, 2022. [Online]. Available: http://infotelesc.kpi.ua/article/view/259751/256220 Search in Google Scholar

[28] X. Bai, Z. Xie, X. Xu, and Y. Xiao, “An adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clustering,” Ocean Engineering, vol. 280, Jul. 2023, Art. no. 114930. https://doi.org/10.1016/j.oceaneng.2023.114930 Search in Google Scholar

[29] V. V. Romanuke, “A prototype model for semantic segmentation of curvilinear meandering regions by deconvolutional neural networks,” Applied Computer Systems, vol. 25, no. 1, pp. 62–69, May 2020. https://doi.org/10.2478/acss-2020-0008 Search in Google Scholar

[30] B. Żak and S. Hożyń, “Local image features matching for realtime seabed tracking applications,” Journal of Marine Engineering & Technology, vol. 16, no. 4, pp. 273–282, Oct. 2017. https://doi.org/10.1080/20464177.2017.1386266 Search in Google Scholar

[31] V. V. Romanuke, “Accurate detection of multiple targets by uniform rectangular array radar with threshold soft update and area rescanning,” Information and Telecommunication Sciences, vol. 13, no. 2, pp. 62–71, Dec. 2022. https://doi.org/10.20535/2411-2976.22022.62-71 Search in Google Scholar

[32] V. V. Romanuke, “Optimization of a dataset for a machine learning task by clustering and selecting closest-to-the-centroid objects,” Herald of Khmelnytskyi National University. Technical Sciences, vol. 1, no. 6, pp. 263–265, 2018. Search in Google Scholar

[33] S. J. Phillips, “Acceleration of K-Means and related clustering algorithms,” in D. M. Mount and C. Stein, Eds., Lecture Notes in Computer Science, vol. 2409, Springer, Jan. 2002, pp. 166–177. https://doi.org/10.1007/3-540-45643-0_13 Search in Google Scholar

[34] V. V. Romanuke, “Parallelization of the traveling salesman problem by clustering its nodes and finding the best route passing through the centroids,” Applied Computer Systems, vol. 28, no. 2, pp. 189–202, Dec. 2023. https://doi.org/10.2478/acss-2023-0019 Search in Google Scholar

[35] C. L. Valenzuela and A. J. Jones, “Evolutionary divide and conquer (I): A novel genetic approach to the TSP,” Evolutionary Computation, vol. 1, no. 4, pp. 313–333, Dec. 1993. https://doi.org/10.1162/evco.1993.1.4.313 Search in Google Scholar

[36] V. V. Romanuke, “Traveling salesman problem parallelization by solving clustered subproblems,” Foundations of Computing and Decision Sciences, vol. 48, no. 4, pp. 453–481, Dec. 2023. https://doi.org/10.2478/fcds-2023-0020 Search in Google Scholar

[37] V. V. Romanuke, “Deep clustering of the traveling salesman problem to parallelize its solution,” Computers & Operations Research, vol. 165, May 2024, Art. no. 106548. https://doi.org/10.1016/j.cor.2024.106548 Search in Google Scholar

[38] T. Gonzalez, “Clustering to minimize the maximum intercluster distance,” Theoretical Computer Science, vol. 38, pp. 293–306, 1985. https://doi.org/10.1016/0304-3975(85)90224-5 Search in Google Scholar

[39] A. Czapiewska, A. Luksza, R. Studanski, and A. Żak, “Reduction of the multipath propagation effect in a hydroacoustic channel using filtration in cepstrum,” Sensors, vol. 20, iss. 3, Jan. 2020, Art. no. 751. https://doi.org/10.3390/s20030751 Search in Google Scholar

[40] Y. Zack, “Cluster analysis for multidimensional objects in fuzzy data conditions,” System research and information technologies, no. 2, pp. 18– 34, Dec. 2021. https://doi.org/10.20535/SRIT.2308-8893.2021.2.02 Search in Google Scholar

[41] G. Grzeczka and M. Klebba, “Automated calibration system for digital multimeters not equipped with a communication interface,” Sensors, 2020, vol. 20, iss. 13, Jun. 2020, Art. no. 3650. https://doi.org/10.3390/s20133650 Search in Google Scholar

[42] J. Zalewski and S. Hożyń, “Computer vision-based position estimation for an autonomous underwater vehicle,” Remote Sensing, vol. 16, iss. 5, Feb. 2024, Art. no. 741. https://doi.org/10.3390/rs16050741 Search in Google Scholar

[43] W. Kaplan, “Maxima and minima with applications: Practical optimization and duality,” in Wiley Series in Discrete Mathematics and Optimization, vol. 51, John Wiley & Sons, 2011, p. 61. Search in Google Scholar

[44] V. V. Romanuke, “Three-point iterated interval half-cutting for finding all local minima of unknown single-variable function,” Electrical, Control and Communication Engineering, vol. 18, no. 1, pp. 27–36, Jun. 2022. https://doi.org/10.2478/ecce-2022-0004 Search in Google Scholar