Parallel algorithms for hierarchical clustering
Parallel Computing
Self-organizing maps
Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
MPI-The Complete Reference, Volume 1: The MPI Core
MPI-The Complete Reference, Volume 1: The MPI Core
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Parallel nearest neighbour clustering algorithm (PNNCA) for segmenting retinal blood vessels
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
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Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Due to the continuous increase of datasets size and the intensive computation of clustering algorithms when used for analyzing large datasets, developing of efficient clustering algorithms is needed for processing time reduction. This paper describes the design and implementation of a recently developed clustering algorithm RACAL [1], which is a RAdius based Clustering ALgorithm. The proposed parallel algorithm (PRACAL) has the ability to cluster large datasets of high dimensions in a reasonable time, which leads to a higher performance computing.