By Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)
This ebook constitutes the refereed court cases of the thirteenth commercial convention on facts Mining, ICDM 2013, held in big apple, long island, in July 2013. The 22 revised complete papers offered have been rigorously reviewed and chosen from 112 submissions. the subjects diversity from theoretical facets of knowledge mining to purposes of knowledge mining, akin to in multimedia info, in advertising, finance and telecommunication, in medication and agriculture, and in approach regulate, and society.
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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
This is challenged by our AAF. To test the AAF we used data from the breath gas community, but the whole system can be used with every other domain. g. sports, news) or every other classiﬁcation problem that can be solved with the provided algorithms. The most important motivation for using the AAF is to improve the productivity. Therefore we must plan for all calculations to be able to (a) use unused resources in the cloud, (b) start new instances if required dynamically, (c) reduce the waiting time as far as possible, (d) generate a report, and (e) keep an eye on the total costs.
Input Data Data Selection Linear Methods Neural Network Data Preperation ... Principal Component Analysis Table Report Fig. 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.
2. 3. 4. Reduce the original data set only vertically. Execute a clustering process over the full set of data to obtain Model2. Label all the original data set and the sample data set with Model1 and Model2. Compare the resulting distribution of elements labeled with both models. The results are discussed in what as follows. An Automated Search Space Reduction Methodology for Large Databases 21 Fig. 5. Graph view of the clustering result supplied by the Miner Comparison of Model1 and Model2 Table 2 shows the percentages for the two models.