Get 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 court cases of the thirteenth commercial convention on information Mining, ICDM 2013, held in ny, manhattan, in July 2013. The 22 revised complete papers awarded have been rigorously reviewed and chosen from 112 submissions. the subjects variety from theoretical features of knowledge mining to functions of information mining, akin to in multimedia information, in advertising and marketing, finance and telecommunication, in drugs and agriculture, and in procedure regulate, 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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Extra 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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G. product) and the needed total costs to produce it [12]. In the following we briefly present a concrete example in oder to show what factors have strong influences on the productivity. In a computer factory the productivity can be calculated by the number of computers produced divided by the needed working hours, or inversely the number of working hours to assemble a computer. The productivity can be increased by increasing the output or decreasing the needed working hours. , equipment), or (c) providing an automated plant.

The common activities use the local or remote Towards a High Productivity Automatic Analysis Framework 29 machines for calculations. The AAF uses Taverna as workflow engine and we provide several new activities. Our statistical algorithm activities use the Code Execution Framework for the calculations and therefore a cloud based infrastructure is used. g. CSV converter) that can easily be used within the provided AAF-workflow. With Taverna the AAF-workflow can be adopted and used for many different purposes.

The names of the clusters were replaced by letters to avoid possible confusions with the neuron numbers shown in the Miner’s results. As table 4 shows, the result clusters are very similar. Table 2. Clusters’ comparison for Model1 and Model2 Clusters A B C D E F Model1 (%) 30 21 15 12 12 10 Model2 (%) 27 20 18 15 12 8 Difference (%) 3 1 3 3 0 2 Clustering from Sampling (Model1) Having the clustering Model1, we labeled the sample data and the full data sets. The resulting distribution of elements into the different 6 clusters was expressed in percentages for comparison effects.

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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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