DBSCAN

Parallel & Scalable DBSCAN

1. Goetz, M., Bodenstein, C., Riedel, M.: HPDBSCAN – Highly Parallel DBSCAN, in conference proceedings of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015), Machine Learning in HPC Environments (MLHPC 2015) Workshop, November 15-20, 2015, Austin, Texas, USA
   [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] [ MORE ]
• Open source available: Download

DBSCAN Applications

1. Bodenstein, C., Goetz, M., Jansen, A., Scholz, H., Riedel, M.: Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay, in conference proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA 2016), December 18-20, 2016, Anaheim, USA
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Social Media

ResearchGate: Nice work, Morris! Your research items reached 6000 reads: most read paper ~600 reads is HPDBSCAN: highly parallel DBSCAN @ SC2015 & used in #MachineLearning #datamining @DEEPprojects @fzj_jsc @fz_juelich @Haskoli_Islands #SMITH

full text: https://t.co/72Ek7SPDK5 pic.twitter.com/TY8L2zuPXj

— Morris Riedel (@MorrisRiedel) July 5, 2019

Sieh dir diesen Beitrag auf Instagram an

ResearchGate: Nice work, Morris! Your research items reached 6000 reads: most read paper ~600 reads is HPDBSCAN: highly parallel DBSCAN @ SC2015 & used in #MachineLearning #datamining @ DEEP Projects #julichsupercomputingcenter @forschungszentrum_juelich @haskoli_islands #SMITH full text: https://www.researchgate.net/publication/301463871_HPDBSCAN_highly_parallel_DBSCAN

Ein Beitrag geteilt von Morris Riedel (@morrisriedel) am Jul 5, 2019 um 4:31 PDT

SMITH Algorithmic Surveillance of Intensive Care Unit Patients (ASIC) workshop @UniklinikAachen @JRC_COMBINE discussing parallel DBSCAN clustering via modular supercomputing by @DEEPprojects @fzj_jsc @fz_juelich @Haskoli_Islands @uisens @uni_iceland – see https://t.co/ZeOko01yZK pic.twitter.com/zm3j3s0kE9

— Morris Riedel (@MorrisRiedel) March 13, 2019

DBSCAN Related Work: HPC and HTC Survey

1. Neukirchen, H.: Elephant against Goliath: Performance of Big Data versus High-Performance Computing DBSCAN Clustering Implementations, in workshop proceedings of the 1st Simulation Science Workshop 2017 (SimScience 2017), Communications in Computer and Information Science (CCIS), Vol. 889, Springer, April 27-28, 2017, Goettingen, Germany
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]

DBSCAN Related Work: PDSDBSCAN (HPC Implementation)

1. Patwary, M.A., Palsetia, D., Agrawal, A., Liao, W., Manne, F., Choudhary, A.: A new scalable parallel DBSCAN algorithm using the disjoint-set data structure, in conference proceedings of the IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2012, November 10-16, 2012, Salt Lake City, USA
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• Open source available: Download

DBSCAN Related Work: Other HPC Approaches

1. Savvas, I.K., Tselios, D.: Parallelizing DBSCAN Algorithm Using MPI, in conference proceedings of the IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2016, June 13-15, 2016, Paris, France
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• Not open source
• Only MPI implementation, not OpenMP or hybrid implementation
2. Hu, X., Huang, J., Qui, M.: A Communication Efficient Parallel DBSCAN Algorithm Based on Parameter Server, in conference proceedings of the ACM Conference on Information and Knowledge Management (CIKM), 2017, November 6-10, 2017, Singapore
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• PS-DBSCAN implementation (not open source)
• Available as Paid service in the Alibaba Cloud Platform of AI (PAI)
• Only MPI implementation, not OpenMP or hybrid implementation

DBSCAN Related Work: Northwestern Apache Spark Implementation

1. Han, D., Agrarwal, A., Liao, W.K., Choudhary, A.: Parallel DBSCAN Algorithm Using a Data Partitioning Strategy with Spark Implementation, in conference proceedings of the IEEE International Conference on Big Data (Big Data), December 10-13, 2018, Seattle, WA, USA
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• Kd-Tree and partitioning strategy implementation using Apache Spark
2. Han, D., Agrarwal, A., Liao, W.K., Choudhary, A.: A Fast DBSCAN Algorithm with Spark Implementation, in book chapter of Big Data in Engineering Applications, 2018, Springer, Part of the Studies in Big Data book series (SBD, volume 44)
   [ DOI ] [ RESEARCHGATE ]
• Kd-Tree and partitioning strategy implementation using Apache Spark

DBSCAN Related Work: Other Apache Spark Implementations

1. Song, H., Lee, J.G.: RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning, in conference proceedings of the ACM SIGMOD/PODS International Conference on Management of Data, June 10-15, 2018, Houston, TX, USA
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• RP-DBSCAN implementation using Apache Spark
• Using real-world data sets on 12 Microsoft Azure machines (48 cores)
2. Shibla, T., Kumar, S.: Improving Efficiency of DBSCAN by Parallelizing kd-Tree Using Spark, in conference proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS), June 14-15, 2018, Madurai, India
   [ EVENT ] [ DOI ] [ RESEARCHGATE ]
• Kd-Tree implementation using Apache Spark