Parallel algorithms for hierarchical clustering
Parallel Computing
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Experiments in Parallel Clustering with DBSCAN
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Parallel k/h-Means Clustering for Large Data Sets
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Large-Scale Parallel Data Clustering
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Data weaving: scaling up the state-of-the-art in data clustering
Proceedings of the 17th ACM conference on Information and knowledge management
Multi-core application performance optimization using a constrained tandem queueing model
Journal of Network and Computer Applications
Nested parallelism in the OMPI OpenmP/C compiler
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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This paper presents a parallel implementation of CURE, an efficient hierarchical data clustering algorithm, using the OpenMP programming model. OpenMP provides a means of transparent management of the asymmetry and non-determinism in CURE, while our OpenMP runtime support enables the effective exploitation of the irregular nested loop-level parallelism. Experimental results for various problem parameters demonstrate the scalability of our implementation and the effective utilization of parallel hardware, which enable the use of CURE for large data sets.