HPDBSCAN: highly parallel DBSCAN

ABSTRACT

Clustering algorithms in the field of data-mining are used to aggregate similar objects into common groups. One of the best-known of these algorithms is called DBSCAN. Its distinct design enables the search for an apriori unknown number of arbitrarily shaped clusters, and at the same time allows to filter out noise. Due to its sequential formulation, the parallelization of DBSCAN renders a challenge. In this paper we present a new parallel approach which we call HPDBSCAN. It employs three major techniques in order to break the sequentiality, empower workload-balancing as well as speed up neighborhood searches in distributed parallel processing environments i) a computation split heuristic for domain decomposition, ii) a data index preprocessing step and iii) a rule-based cluster merging scheme.

As a proof-of-concept we implemented HPDBSCAN as an OpenMP/MPI hybrid application. Using real-world data sets, such as a point cloud from the old town of Bremen, Germany, we demonstrate that our implementation is able to achieve a significant speed-up and scale-up in common HPC setups. Moreover, we compare our approach with previous attempts to parallelize DBSCAN showing an order of magnitude improvement in terms of computation time and memory consumption.

References

1. H. Abdi. Coefficient of variation. Encyclopedia of research design, pages 169--171, 2010.
2. G. M. Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. In Proceedings of the spring joint computer conference 1967, pages 483--485. ACM, 1967.
3. D. Arlia and M. Coppola. Experiments in parallel clustering with dbscan. In Euro-Par 2001 Parallel Processing, pages 326--331. Springer, 2001.
4. J. L. Bentley and J. H. Friedman. Data structures for range searching. ACM Computing Surveys (CSUR), 11(4):397--409, 1979.
5. S. Brecheisen, H.-P. Kriegel, and M. Pfeifle. Parallel density-based clustering of complex objects. In Advances in Knowledge Discovery and Data Mining, pages 179--188. Springer, 2006.
6. C. Cantrell. Modern mathematical methods for physicists and engineers. CUP, 2000.
7. M. Chen, X. Gao, and H. Li. Parallel DBSCAN with Priority R-Tree. In Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference, pages 508--511. IEEE, 2010.
8. L. Dagum and R. Menon. OpenMP: an industry standard API for shared-memory programming. Computational Science & Engineering, IEEE, 5(1):46--55, 1998.
9. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226--231, 1996.
10. Forschungszentrum Jülich GmbH. Juelich Dedicated GPU Environment. http://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUDGE/JUDGE_node.html.
11. Forschungszentrum Jülich GmbH. Juelich Storage Cluster. http://www.fz-juelich.de/ias/jsc/EN/Expertise/Datamanagement/OnlineStorage/JUST/JUST_node.html.
12. I. Foster. Designing and building parallel programs. Addison Wesley Publishing Company, 1995.
13. Y. X. Fu, W. Z. Zhao, and H. F. Ma. Research on parallel dbscan algorithm design based on mapreduce. Advanced Materials Research, 301:1133--1138, 2011.
14. Götz, Markus and Bodenstein, Christian. HPDBSCAN Benchmark test files. http://hdl.handle.net/11304/6eacaa76-c275-11e4-ac7e-860aa0063d1f.
15. W. Gropp, E. Lusk, and A. Skjellum. Using MPI: portable parallel programming with the message-passing interface, volume 1. MIT press, 1999.
16. S. Habib, V. Morozov, H. Finkel, A. Pope, K. Heitmann, K. Kumaran, T. Peterka, J. Insley, D. Daniel, P. Fasel, et al. The universe at extreme scale: multi-petaflop sky simulation on the bg/q. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, page 4. IEEE Computer Society Press, 2012.
17. J. Han, M. Kamber, and J. Pei. Data Mining: concepts and techniques - 3rd ed. Morgan Kaufmann, 2011.
18. HDF Group. Hierachical Data Format 5. http://www.hdfgroup.org/HDF5.
19. Y. He, H. Tan, W. Luo, H. Mao, D. Ma, S. Feng, and J. Fan. MR-DBSCAN: an efficient parallel density-based clustering algorithm using mapreduce. In Parallel and Distributed Systems, 2011 IEEE 17th International Conference, pages 473--480, 2011.
20. Jacobs University Bremen. 3D Scan Repository. http://kos.informatik.uni-osnabrueck.de/3Dscans/.
21. A. Knöpfel, B. Gröne, and P. Tabeling. Fundamental modeling concepts, volume 154. Wiley, UK, 2005.
22. T. Leng, R. Ali, J. Hsieh, V. Mashayekhi, and R. Rooholamini. An empirical study of hyper-threading in high performance computing clusters. Linux HPC Revolution, 2002.
23. Markus Götz. HPDBSCAN implementation. https://bitbucket.org/markus.goetz/hpdbscan.
24. Northwestern University. Parallel netCDF. http://cucis.ece.northwestern.edu/projects/PnetCDF/.
25. M. M. A. Patwary, D. Palsetia, A. Agrawal, et al. A new scalable parallel dbscan algorithm using the disjoint-set data structure. In High Performance Computing, Networking, Storage and Analysis (SC), International Conference for, pages 1--11. IEEE, 2012.
26. D. Šidlauskas, S. Šaltenis, C. W. Christiansen, J. M. Johansen, and D. Šaulys. Trees or grids?: indexing moving objects in main memory. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 236--245. ACM, 2009.
27. X. XU, J. JAGER, and H.-P. KRIEGEL. A Fast Parallel Clustering Algorithm for Large Spatial Databases. Data Mining and Knowledge Discovery, 3:263--290, 1999.
28. A. Zhou, S. Zhou, J. Cao, Y. Fan, and Y. Hu. Approaches for scaling dbscan algorithm to large spatial databases. Journal of computer science and technology, 15(6):509--526, 2000.

Index Terms

• General and reference

  • Document types

    • Reference works

• Information systems

  • Information systems applications

    • Data mining