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 e-book constitutes the refereed lawsuits of the thirteenth commercial convention on information Mining, ICDM 2013, held in manhattan, new york, in July 2013. The 22 revised complete papers provided have been conscientiously reviewed and chosen from 112 submissions. the subjects diversity from theoretical elements of information mining to purposes of knowledge mining, reminiscent of in multimedia information, in advertising and marketing, finance and telecommunication, in medication and agriculture, and in technique keep watch over, and society.

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These PRBS tests can also be used for profile tracking in batch processes, where the aim is to identify the influence of the MVs on other online process variables [14, 15]. In these two cases, the effect of each set of MV changes on the other online measurements is observed quasi immediately. Batch-end quality prediction faces a few extra problems. 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.

Recently, McCready [17] optimized the yield of a batch process by adapting the manipulated variables (MVs) at three distinct decision moments and predicting the resulting batch-end quality with a Partial Least Squares (PLS [18]) statistical inference model. For full (online) optimization and disturbance rejection, however, more decision moments are needed. In an ideal case, changes to the MVs are made every few time points: frequently enough to tightly control the final quality and negate the effect of process disturbances, but without upsetting the batch with too frequent adjustments.

2. AAF-Workflow Data Analysis Report Generation 30 T. Ludescher et al. In the data preparation part the scientist is able to select the statistical analysis type (classification, prediction, or clustering), and define the independent and dependent variables. g. linear methods, neural network, principal component analysis). The report generation part uses the results of the analytical methods, orders the results (best classification models on top), and generates an HTML report. input data variable sets k repeat for all variable sets create k bins k-1 bins train group repeat k times train model 1 bin test group model test model train full model % of correctly classified samples model test full model % of correctly classified samples calculate Average/SD Average/SD Fig.

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