By Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)
This ebook constitutes the refereed lawsuits of the thirteenth business convention on facts Mining, ICDM 2013, held in big apple, manhattan, in July 2013. The 22 revised complete papers offered have been conscientiously reviewed and chosen from 112 submissions. the subjects diversity from theoretical features of information mining to functions of knowledge mining, corresponding to in multimedia info, in advertising and marketing, finance and telecommunication, in medication and agriculture, and in approach keep watch over, and society.
Read 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 resources for Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings
G. g. independent variables), to optimize and increase the productivity. It further shows how these topics are inﬂuencing each other in the context of the AAF. , number of worker nodes). With time consuming calculations the productivity can be increased by using the AAF in several diﬀerent ways as follows: – The total costs can be reduced due to (a) reduction of license/software costs, (b) reduction of maintenance costs, (c) reduction of ownership costs, (d) reduction of the hardware cost, and (e) reduction of the personnel cost.
Hence, once the full PLS model is identiﬁed, online estimates are available without much extra eﬀort. Other options for online batch-end quality estimation include the use of evolving models [20–22]. However, it has been shown that TSR provides accurate estimates of the batch-end quality even when few samples are available despite its simple linear nature [19, 23]. 4 Batch Control and Optimization McCready optimized the yield of a batch process with an MPLS model by adapting manipulated variables (MVs) at three distinct decision moments .
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. It is possible to detect overﬁtting if (a) the mean value of the cross validation is much better than the main value of the complete model or (b) the standard deviation is high. The AAF is able to calculate and evaluate a lot of diﬀerent models (see Section 5). Therefore it is upmost important that the system is able to preselect/order the results.