By David J. Hand (auth.), Allan Tucker, Frank Höppner, Arno Siebes, Stephen Swift (eds.)
This booklet constitutes the refereed convention court cases of the twelfth foreign convention on clever information research, which was once held in October 2013 in London, united kingdom. The 36 revised complete papers including three invited papers have been rigorously reviewed and chosen from eighty four submissions dealing with all types of modeling and research equipment, regardless of self-discipline. The papers hide all elements of clever information research, together with papers on clever help for modeling and interpreting info from advanced, dynamical systems.
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Additional info for Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013. Proceedings
To visualize our approach, we plotted all computed correlation coefficients with all available replicates time points in the training set for each time point where each patient is illustrated with a different colour. The plotted correlation points provide us with Time Point Estimation of a Single Sample from High Throughput Experiments 41 a primary idea about the behavior of the computed correlation coefficient depending on the time point we intend to estimate. The average of the selected time points with the highest correlation from each patient of the training set is displayed as the estimated time point.
But this usually applies neither to the time points nor to the measurement values. But we would expect that at similar time points, replicates would have roughly the same values. It should be noted that there can also be missing values. The main objective of this work is to develop a method in order to estimate the time point for a single sample using an independent data set of repeated replicates from time-resolved data, measured from high-throughput experiments. To achieve reliable results, two aspects must be considered.
Data Mining and Knowledge Discovery 23(3), 407–446 (2011) 3. : A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers 100(9), 881–890 (1974) 4. : Tiling databases. , Arikawa, S. ) DS 2004. LNCS (LNAI), vol. 3245, pp. 278–289. Springer, Heidelberg (2004) 5. : Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3), 9 (2006) 6. : Assessing data mining results via swap randomization. ACM Transactions on Knowledge Discovery from Data 1(3), 14 (2007) Subjective Interestingness in Exploratory Data Mining 31 7.
Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013. Proceedings by David J. Hand (auth.), Allan Tucker, Frank Höppner, Arno Siebes, Stephen Swift (eds.)