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SPARQL is the W3C [Prud and Seaborne, 2006] recommendation for querying datasets structured according to the Resource Description Framework (RDF) [Lassila and Swick, 1998].
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Sixteen artificial datasets have been generated covering all the combinations of these four binary meta-descriptors expecting to simulate a wide range of real datasets' structures arising in classification tasks.
We applied the radial basis function (RBF) as the kernel types in accordance with our datasets structure, where γ > 0 is the kernel parameter as shown in (4) k (x i, x j ) = e − γ | x 1 − x j | 2. Subsequently, these datasets were mapped into the linearly separable space, and hyperplanes divided them into two labeled classes.
The standard K-means clustering algorithm is suited for application to small datasets and structured data.
As part of NHS primary and secondary care services, the GP and PEDW datasets are structured to include an NHS number.
Once the datasets were structured and examined for missing values, a small experiment was implemented to determine whether absolute or log power performed better.
The concurrency dataset was structured with six observations for each individual representing each of the preceding 6 months.
The dataset is structured to contain a row for each day in the historical file for all influenzalike illness (ILI) visits in the clinic network.
Fig. 8 The dataset structure of the SciData Data Model.
Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure.
The method is inspired by the recent Pairwise Recommendation [9] algorithm, which considers the intrinsic dataset structure through a recommendation simulation model.
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