https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16283 Sat 24 Mar 2018 07:59:26 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16267 Sat 24 Mar 2018 07:54:14 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21225 k-nearest neighbor (kNN) structure can provide an efficient solution to this problem. Only a few attempts were found in the literature that focus on solving the problem using the kNNs. This paper briefs the state-of-the-art strategies for the MST problem and a fast and scalable solution combining the classical Borůvka’s MST algorithm and the kNN graph structure. The proposed algorithm is implemented for CUDA enabled GPUs (kNN-Borůvka-GPU), but the basic approach is simple and adaptable to other available architectures. Speed-ups of 30-40 times compared with CPU implementation was observed for several large-scale synthetic and real world data sets.]]> Sat 24 Mar 2018 07:53:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:26041 Sat 24 Mar 2018 07:26:27 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22916 kNN graphs from DNA microarray data sets. The relationship among the genes in the kNN graph is determined by the similarity of their expression levels. The proposed method has been applied to a well known breast cancer microarray study and we highlighted the correlation of the highly ranked genes to the time to relapse of the disease. The method is readily applicable to other datasets, where the data points can be recognised in a multidimensional space. It can be applied to other networks (e.g., social networks, the Internet, etc.) with minimal modications.]]> Sat 24 Mar 2018 07:16:28 AEDT ]]>