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This method visualises the temporality of interrelations between interlinked variables and, as such, can be applied to a range of datasets.
Since submitters vary in their laboratory procedures in regards to how samples are prepared and how the NMR data is acquired, a range of datasets varying in spectral purity were received.
This allowed us to assess the predictive power of AAC over a range of datasets and classifier types.
We generated a range of datasets containing between 10 and 1000 instances.
Bootstrapping showed that similar nonlinear relationships were preserved across a range of datasets.
We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae.
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We benchmark our algorithms to both simulated and biological datasets and demonstrate the dramatic improvements in runtime at a range of dataset sizes.
In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets.
We evaluate our method on a range of synthetic datasets modeled after real cancer microarrays, and then on four biological datasets of breast, uveal melanoma and bladder tumors.
The algorithms are applied to a range of 76 datasets (see "Experimental" section).
We tested the algorithm on a range of different datasets extracted from a number of documented benchmarks and test cases.
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