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.
Read Online or Download Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings PDF
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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
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 . 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 . 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).