Page 93 - 《软件学报》2026年第3期
P. 93

1056                                                       软件学报  2026  年第  37  卷第  3  期


                 [21]   Lulli A, Dell'Amico M, Michiardi P, Ricci L. NG-DBSCAN: Scalable density-based clustering for arbitrary data. Proc. of the VLDB
                     Endowment, 2016, 10(3): 157–168. [doi: 10.14778/3021924.3021932]
                 [22]   Chen YG, Ruys W, Biros G. KNN-DBSCAN: A DBSCAN in high dimensions. ACM Trans. on Parallel Computing, 2025, 12(1): 3. [doi:
                     10.1145/3701624]
                 [23]   Mustafa H, Leal E, Gruenwald L. An experimental comparison of GPU techniques for DBSCAN clustering. In: Proc. of the 2009 IEEE
                     Int’l Conf. on Big Data. Los Angeles: IEEE, 2019. 3701–3710. [doi: 10.1109/BigData47090.2019.9006169]
                 [24]   Nagarajan V, Kulkarni M. RT-DBSCAN: Accelerating DBSCAN using ray tracing hardware. In: Proc. of the 2023 IEEE Int’l Parallel
                     and Distributed Processing Symp. St. Petersburg: IEEE, 2023. 963–973. [doi: 10.1109/IPDPS54959.2023.00100]
                 [25]   Wu GQ, Cao LQ, Tian HY, Wang W. HY-DBSCAN: A hybrid parallel DBSCAN clustering algorithm scalable on distributed-memory
                     computers. Journal of Parallel and Distributed Computing, 2022, 168: 57–69. [doi: 10.1016/j.jpdc.2022.06.005]
                 [26]   Loke SC, MacDonald BA, Parsons M, Wünsche BC. Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm.
                     Journal of Real-time Image Processing, 2021, 18(6): 2361–2376. [doi: 10.1007/s11554-021-01128-5]
                 [27]   Cayton L. Accelerating nearest neighbor search on manycore systems. In: Proc. of the 26th IEEE Int’l Parallel and Distributed Processing
                     Symp. Shanghai: IEEE, 2012. 402–413. [doi: 10.1109/IPDPS.2012.45]
                 [28]   Poudel M, Gowanlock M. CUDA-DClust+: Revisiting early GPU-accelerated DBSCAN clustering designs. In: Proc. of the 28th IEEE Int’
                     l Conf. on High Performance Computing, Data, and Analytics. Bengaluru: IEEE, 2021. 354–363. [doi: 10.1109/HiPC53243.2021.00049]
                 [29]   Welton B, Samanas E, Miller BP. Mr. Scan: Extreme scale density-based clustering using a tree-based network of GPGPU nodes. In:
                     Proc. of the 2013 Int’l Conf. on High Performance Computing, Networking, Storage and Analysis. Denver: IEEE, 2013. 1–11. [doi: 10.
                     1145/2503210.2503262]
                 [30]   Chen YW, Zhou LD, Pei SW, Yu ZW, Chen Y, Liu X, Du JX, Xiong NX. KNN-BLOCK DBSCAN: Fast clustering for large-scale data.
                     IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2021, 51(6): 3939–3953. [doi: 10.1109/TSMC.2019.2956527]
                 [31]   Chen Q, Zhao B, Wang HD, Li MQ, Liu CJ, Li ZZ, Yang M, Wang JD. SPANN: Highly-efficient billion-scale approximate nearest
                     neighbor search. In: Proc. of the 35th Int’l Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021. 398.
                 [32]   Gan JH, Tao YF. DBSCAN revisited: Mis-claim, un-fixability, and approximation. In: Proc. of the 2015 ACM SIGMOD Int’l Conf. on
                     Management of Data. Melbourne: ACM, 2015. 519–530. [doi: 10.1145/2723372.2737792]
                 [33]   Wu YP, Guo JJ, Zhang XJ. A linear DBSCAN algorithm based on LSH. In: Proc. of the 2007 Int’l Conf. on Machine Learning and
                     Cybernetics. Hong Kong: IEEE, 2007. 2608–2614. [doi: 10.1109/ICMLC.2007.4370588]
                 [34]   Keramatian A, Gulisano V, Papatriantafilou M, Tsigas P. IP.LSH.DBSCAN: Integrated parallel density-based clustering through locality-
                     sensitive hashing. In: Proc. of the 28th European Conf. on Parallel and Distributed Computing (Euro-Par 2022). Glasgow: Springer, 2022.
                     268–284. [doi: 10.1007/978-3-031-12597-3_17]
                 [35]   Chen YW, Zhou LD, Bouguila N, Wang C, Chen Y, Du JX. BLOCK-DBSCAN: Fast clustering for large scale data. Pattern Recognition,
                     2021, 109: 107624. [doi: 10.1016/j.patcog.2020.107624]
                 [36]   Patwary MMA, Satish N, Sundaram N, Manne F, Habib S, Dubey P. Pardicle: Parallel approximate density-based clustering. In: Proc. of
                     the 2014 Int’l Conf. for High Performance Computing, Networking, Storage and Analysis. New Orleans: IEEE, 2014. 560–571. [doi: 10.
                     1109/SC.2014.51]
                 [37]   Jiang H, Jang J, Łącki J. Faster DBSCAN via subsampled similarity queries. In: Proc. of the 34th Int’l Conf. on Neural Information
                     Processing Systems. Vancouver: Curran Associates Inc., 2020. 1879.
                 [38]   Malkov YA, Yashunin DA. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs.
                     IEEE Trans. on Pattern Analysis and Machine Intelligence, 2020, 42(4): 824–836. [doi: 10.1109/TPAMI.2018.2889473]
                 [39]   Böhm C, Noll R, Plant C, Wackersreuther B. Density-based clustering using graphics processors. In: Proc. of the 2009 ACM Conf. on
                     Information and Knowledge Management. Hong Kong: ACM, 2009. 661–670. [doi: 10.1145/1645953.1646038]
                 [40]   Cal  P,  Woźniak  M.  Data  preprocessing  with  GPU  for  DBSCAN  algorithm.  In:  Burduk  R,  Jackowski  K,  Kurzynski  M,  Wozniak  M,
                     Zolnierek A, eds. Proc. of the 8th Int’l Conf. on Computer Recognition Systems (CORES 2013). Heidelberg: Springer, 2013. 793–801.
                     [doi: 10.1007/978-3-319-00969-8_78]
                 [41]   Andrade  G,  Ramos  G,  Madeira  D,  Sachetto  R,  Ferreira  R,  Rocha  L.  G-DBSCAN:  A  GPU  accelerated  algorithm  for  density-based
                     clustering. Procedia Computer Science, 2013, 18: 369–378. [doi: 10.1016/j.procs.2013.05.200]
                 [42]   Loh WK, Moon YS, Park YH. Fast density-based clustering using graphics processing units. IEICE Trans. on Information and Systems,
                     2014, E97(5): 1349–1352. [doi: 10.1587/transinf.E97.D.1349]
                 [43]   Qian Q, Zhao S, Xiao CJ, Hung CL. Multi-level grid based clustering and GPU parallel implementations. In: Proc. of the 14th Int’l Symp.
                     on Pervasive Systems, Algorithms and Networks & the 11th Int’l Conf. on Frontier of Computer Science and Technology & the 3rd Int’l
   88   89   90   91   92   93   94   95   96   97   98