Advances in Data Mining. Applications and Theoretical by Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra

By Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)

This ebook constitutes the refereed complaints of the thirteenth commercial convention on facts Mining, ICDM 2013, held in long island, long island, in July 2013. The 22 revised complete papers awarded have been rigorously reviewed and chosen from 112 submissions. the subjects diversity from theoretical features of knowledge mining to purposes of knowledge mining, corresponding to in multimedia info, in advertising, finance and telecommunication, in medication and agriculture, and in method regulate, and society.

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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings

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First of all, the influence of all changes in the MVs is combined in a single quality measurement only available after batch completion. To obtain clear results, only a single combination of MV changes should be tested in each batch. This is a big difference with continuous processes, where different combinations of MV changes can be tested in rapid succession. Hence, the required tests for model identification are very time-consuming. Furthermore, the dynamics of a batch process and the (magnitude of the) influence of the MVs on the final quality change over time.

The result of one calculation is the percentage of correctly Towards a High Productivity Automatic Analysis Framework 31 classified samples. These steps will be repeated k-times (folds) for each sub sample. Afterwards we calculate the average and the standard deviation of the k results folds . Additionally we used the complete data set to train and test the model. The output of one calculation is therefore (a) the percentage of correctly classified samples of the complete model, (b) the average value of the cross validation, and (c) the standard deviation of the cross validation.

Hence, the influence of the entire batch history on the final quality is captured. 2 Partial Least Squares First, the main nonlinear and dynamic components are removed from the data by mean-centering and scaling of batch-wise unfolded data [7]. This effectively linearizes the measurements around their average trajectory, improving modelling results because PLS is multi-linear [7, 8]. Next, a standard PLS model identifies the relation between online measurements and final quality [18]. PLS is a multi-linear latent variable modelling approach that decomposes the input matrix X and output (quality) matrix Y into R uncorrelated latent variables (components).

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