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Loai Abdallah and Ilan Shimshoni

References Abdallah, L. and Shimshoni, I. (2013). An ensemble-clustering-based distance metric and its applications, International Journal of Business Intelligence and Data Mining 8(3): 264-287. Abdallah, L. and Shimshoni, I. (2014). Mean shift clustering algorithm for data with missing values, 14th International Conference of DaWaK, Munich, Germany, pp. 426-438. Abdallah, L. and Shimshoni, I. (2016). k-means over incomplete datasets using mean Euclidean distance, 12th International Conference on Machine

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Krzysztof Simiński

References Acuña, E. and Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy, in D. Banks, L. House, F. McMorris, P. Arabie and W. Gaul (Eds.), Classification, Clustering and Data Mining Applications , Springer, Berlin/Heidelberg, pp. 639-648. Box, G. E. P. and Jenkins, G. (1970). Time Series Analysis, Forecasting and Control , Holden-Day, Oakland, CA. Chan, L. S., Gilman, J. A. and Dunn, O. J. (1976). Alternative approaches to missing

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Sergio Arciniegas-Alarcón, Marisol García-Peña, Wojtek Janusz Krzanowski and Carlos Tadeu dos Santos Dias

References Arciniegas-Alarcón S., García-Peña M., Dias C.T.S. (2011): Data imputation in trials with genotype×environment interaction. Interciencia 36(6): 444-449. Arciniegas-Alarcón S., García-Peña M., Dias C.T.S., Krzanowski W.J. (2010): An alternative methodology for imputing missing data in trials with genotypeby- environment interaction. Biometrical Letters 47(1): 1-14. Bergamo G.C., Dias C.T.S., Krzanowski W.J. (2008): Distribution-free multiple imputation in an interaction matrix through singular value

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Artur Matyja and Krzysztof Siminski

References [1] Acuña E., Rodriguez C., The treatment of missing values and its effect in the classifier accuracy. In Banks D., House L., McMorris F. R., Arabie P., Gaul W. (eds.), editors, Classification, Clustering and Data Mining Applications, Springer, Berlin, Heidelberg, 2004, 639-648. [2] Alcalá-Fdez J., Fernandez A., Luengo J., Derrac J., García S., Sánchez L., Herrera F., KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic

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Andrea Harnos, Tibor Csörgő and Péter Fehérvári

Templ, M., Alfons, A., Kowarik, A. & Prantner, B. 2015. VIM: Visualization and imputation of missing values. – CRAN Xie, Y. 2014. knitr: A comprehensive tool for reproducible research in R. – In: Stodden, V., Leisch, F. & Peng, R. D. (eds.) Implementing reproducible computational research. – Chapman and Hall/CRC Xie, Y. 2015. Dynamic documents with R and knitr. – Chapman and Hall/CRC, Boca Raton 2 nd ed. Xie, Y. 2016. knitr: A general-purpose package for dynamic report generation in R. – URL: http://yihui.name/knitr/ , R package version 1.15.1

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Julianne M. Edwards and W. Holmes Finch

imputation in the presence of interaction effects. Computational Statistics and Data Analysis, 72, 92 – 104. Eisemann, N., Waldmann, A., & Katalinic, A. (2011). Imputation of missing values of tumour stage in population-based cancer registration. BMC Medical Research Methodology, 11 (129). doi:10.1186/1471-2288-11-129 Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8 (1), 128-141. Enders, C. K. (2004). The impact of missing data on sample reliability estimates

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Dilip C. Nath, Ramesh K. Vishwakarma and Atanu Bhattacharjee

R eferences Daniels M.J., Hogan J.W. (2008): Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. CRC Press. Diggle P., Kenward M.G. (1994): Informative drop-out in longitudinal data analysis. Applied statistics pages 49–93. Dragset I.G. (2009): Analysis of longitudinal data with missing values. Project Thesis. Norwegian University of Scineces and Technology. Fitzmaurice G., Davidian M., Verbeke G., Molenberghs G. (2008): Longitudinal data analysis. CRC Press. Graham J.W. (2012

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Ivan Jordanov, Nedyalko Petrov and Alessio Petrozziello

. Integrated Computer-Aided Engineering, 16(1), 2009, 51-60. [9] E. Granger, M. Rubin, S. Grossberg, P. Lavoie, A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks, 14 (3), 2001, 325-344. [10] S. Maytal, F. Provost, Handling missing values when applying classification models. Journal of Machine Learning Research, 8, 2007, 1625-1657. [11] N. Ibrahim, R. Abdullah, M. Saripan, Artificial neural network approach in radar target classification. Journal of Computer Science, 5(1), 2009, 23. [12] M

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Magda Zupančič

References Becker, G. (2017). Human capital. The people’s champion . Retrieved from https://www.economist.com/printedition/2017-08-05 Berkowitz, S. (2001). Measuring and reporting human capital. Journal of Government Financial Management. Business Source Complete . EBSCO (pp. 13-17). Bohinc, R. (2016). Družbena odgovornost (Social Responsibility). Fakulteta za družbene vede. Ljubljana: Založba FDV. Dean, P., McKenna, K., & Krishan, V. (2012). Accounting for human capital: Is the Balance sheet missing something? International Journal of

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Walter Timo de Vries and Winrich Voß

, topography, socio-economic indicators, long-term vacant land or buildings) are combined with soft data (e.g. on social preferences and cultural landscapes). 4 Discussion: The common ground and missing links When comparing and aligning the different value logics, it is necessary to consider how to align the epistemic logic of different value systems, how to make use of and benefit from existing variations in utilizing value systems at different scales, and the manner in which decisions are prepared and executed. 4.1 Aligning the epistemic logics of different value