Read e-book online Advances in Data Mining. Applications and Theoretical PDF

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

ISBN-10: 3642397352

ISBN-13: 9783642397356

ISBN-10: 3642397360

ISBN-13: 9783642397363

This publication constitutes the refereed lawsuits of the thirteenth business convention on information Mining, ICDM 2013, held in big apple, long island, in July 2013. The 22 revised complete papers provided have been conscientiously reviewed and chosen from 112 submissions. the themes diversity from theoretical points of information mining to purposes of information mining, similar to in multimedia information, in advertising, finance and telecommunication, in drugs and agriculture, and in method keep an eye on, and society.

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

Sample text

For some goods, continuous production is impossible due to chemical constraints. One of the major challenges in operating a batch process is accurately controlling the final quality. First of all, a batch process is dynamic by design. In addition, measurements of the quality parameter are often simply not available online: a sensor for the quality parameter might not exist (yet), it might be too expensive, or the quality might not be present yet. Hence, feedback control is not possible because the current quality of the batch cannot be easily assessed.

Accrue Software Inc. (2002) 5. : Segmentation Problems. Journal of the ACM 51(2), 263–280 (2004) 6. : CURE: An efficient clustering algorithm for Large Databases. In: ACM SIGMOD Proceedings, pp. 73–84 (1998) 7. : New unsupervised clustering algorithm for large datasets. In: ACM SIGKDD Proceedings, pp. 643–648 (2003) 8. : Efficient and Effective Clustering Methods for Spatial Data Mining. In: 20th International Conference on Very Large Data Bases, pp. 144–155 (1994) 9. : A Divide-and-Merge Methodology for Clustering.

For the execution of the analytical methods, in particular for the computation of the results, we use our own cloud-based Code Execution Framework (CEF). It provides web services to execute problem solving environment code, such as MATLAB, Octave, and R scripts, in parallel in the cloud. This combination of the AAF and CEF enables scientists to easily conduct time-consuming calculations without the need to manually combine potential combinations of independent variables. It furthermore automatically evaluates all identified models and provides service for the scientists conducting the analysis.

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Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings by Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)


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