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The case study on a practical retail business, a number of customer purchase record datasets is employed to show our design ideas and explain working principles of the proposed data mining engine.
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Similar to [18], two datasets are employed.
Two simulated datasets were employed in the current paper.
Two sets of datasets were employed in this study.
Four days of datasets were employed to develop the model and one day of dataset was employed to test the prediction accuracy.
Rotating machinery datasets are employed to demonstrate the effectiveness of the proposed method.
As with the biological data, these datasets were employed previously with PCA approach alone [37].
These datasets were employed for descriptor calculation and development of the models.
Total 38 training datasets and 10 testing datasets were employed for training and testing of the ANN respectively.
Nine UCI datasets and five fault recognition datasets are employed as test objects, and the obtained feature subsets are then used for subsequent classification and clustering.
In particular, the GTZAN [17], ISMIR, Homburg [54], Unique [16], and 1517-Artists [16] datasets are employed for music genre classification, the MTV dataset [15] for music mood classification, and the CAL500 dataset [1] for music tagging.
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