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Our implementation results using Beowulf B1 achieved better execution times.
KM-I2C generated better execution times than the other algorithms.
The purpose of this approach is to automate the learn-free facial landmark detection and provide better execution times.
This dataset was used to investigate and explore documents to conduct a cluster analysis of unstructured data with better execution times.
We have introduced a KH-HMR algorithm to make use of parallelization tools through Hadoop and to obtain better execution times than those of the standard K-means approach.
Robson et al. [27] focus on problems faced when clustering large datasets e.g., obtaining efficient clusters over better execution times for large datasets) and propose a parallel clustering (ParC) method for data partitioning that leverages data localities in MapReduce to cluster large datasets.
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The experimental results reveal that the proposed algorithm is effective and yields a better execution time when compared with others.
The metric also facilitates certain trade-offs such as achieving optimal capacity using all nodes or better execution time using a partial set of nodes (expected winners).
It is noted that the P2D-based model showed better execution time than the SPD-based model.
Taken into consideration on the compute intensive characteristics of TSS, we investigate the best method to parallelize it for better execution time.
It can also be seen that, for doLogin, Cross-Tier has 25% better execution time compared to Split-Code, showing the effectiveness of cross-tier partitioning compared to partitioning only at the application-tier.
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