The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Clustering techniques for large data sets—from the past to the future
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
SkIE: a heterogeneous environment for HPC applications
Parallel Computing - Special Anniversary issue
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
High-Performance Data Mining with Skeleton-based Structured ParallelProgramming
High-Performance Data Mining with Skeleton-based Structured ParallelProgramming
Scalable clustering algorithm for N-body simulations in a shared-nothing cluster
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Parallelization of a hierarchical data clustering algorithm using OpenMP
IWOMP'05/IWOMP'06 Proceedings of the 2005 and 2006 international conference on OpenMP shared memory parallel programming
Future Generation Computer Systems
Journal of Parallel and Distributed Computing
Improving DBSCAN's execution time by using a pruning technique on bit vectors
Pattern Recognition Letters
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Scalable parallel OPTICS data clustering using graph algorithmic techniques
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Learning probabilistic real-time automata from multi-attribute event logs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report preliminary results of the application running on a Beowulf with good efficiency.