Methodology. Sage Publications Wolf, Joye, Smith and Fu. Thousand Oaks. CA, 255–268.
Duprey, M., J. Murphy, P. Biemer, and R. Chew. 2017. “Veni, Vidi, Vici: Interactive DataVisualizations for Adaptive Total Design.” Presented at the 5th Workshop on Adaptive and Responsive Survey Design. Ann Arbor, MI.
Eddy, W.F. and Marton, K., Editors. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys . Washington D.C.: The National Academies Press.
Edgar, J., J. Murphy, and M. Keating. 2016
This article presents a visual method for representing the complex labor market internal structure from the perspective of similar occupations based on shared skills; and a prototype tool for interacting with the visualization, together with an extended description of graph construction and the necessary data processing for linking multiple heterogeneous data sources. Since the labor market is not an isolated phenomenon and is constantly impacted by external trends and interventions, the presented method is designed to enable adding extra layers of external information. For instance, what is the impact of a megatrend or an intervention on the labor market? Which parts of the labor market are the most vulnerable to an approaching megatrend or planned intervention? A case study analyzing the labor market together with the megatrend of job automation and computerization is presented. The source code of the prototype is released as open source for repeatability.
Murrell, P. 2011. R Graphics , 2 nd ed. Boca Raton, FL: Chapman & Hall/CRC Press.
Sarkar, D. 2008. Lattice: Multivariate DataVisualization with R . New York: Springer-Verlag.
Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis , 2 nd ed. New York: Springer-Verlag.
Tomasz Rutkowski, Krystian Łapa and Radosław Nielek
mechanism for datavisualization with TSK-type preprocessed collaborative fuzzy rule based system, Journal of Artificial Intelligence and Soft Computing Research 7 (1): 33–46.
Riid, A. and Preden, J.-S. (2017). Design of fuzzy rule-based classifiers through granulation and consolidation, Journal of Artificial Intelligence and Soft Computing Research 7 (2): 137–147.
Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning , Studies in Fuzziness and Soft Computing, Springer Verlag, New York, NY.
Rutkowski, L. (2004). Flexible Neuro
confirmation measures be useful for rough set decision rules?, Engineering Applications of Artificial Intelligence 17(4): 345-361.
Greco, S., Słowi´nski, R. and Szcz˛ech, I. (2012). Properties of rule interestingness measures and alternative approaches to normalization of measures, Information Sciences 216: 1-16.
Healey, C. (1996). Choosing effective colors for datavisualization, Proceedings of the 7th Conference on Visualization, VIS’96, San Francisco, CA, USA, pp. 263-270.
Hernández-Orallo, J., Flach, P.A. and Ramirez, C
Atkinson, A.C., and M. Riani. 2000. Robust Diagnostic Regression Analysis. New York: Springer-Verlag.
Atkinson, A.C., and M. Riani. 2004. “The Forward Search and DataVisualization.” Computational Statistics 19: 29-54.
Bates, D., M. Maechler, B. Bolker and S. Walker. 2014. “lme4: Linear Mixed-Effects Models Using Eiqen and S4. R package version 1.1-7.” Available at: http://CRAN.R-project.org/package= lme4 (accessed February 2, 2015).
Belsley, D.A., R. E. Kuh, and R. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and
Grzegorz Chmaj, Krzysztof Walkowiak, Michał Tarnawski and Michał Kucharzak
., Asioli, S., Celetto, L., Izquierdo, E. and Rovati, F. (2011). Peer-to-peer streaming of scalable video in future Internet applications, IEEE Communications Magazine 49 (3): 128-135, DOI: 10.1109/MCOM.2011.5723810.
Samanta, R., Funkhouser, T. and Li, K. (2001). Parallel rendering with k-way replication, in S.N. Spencer (Ed.), Proceedings of the IEEE 2001 Symposium on Parallel and Large-DataVisualization and Graphics, PVG’01 , IEEE Press, Piscataway, NJ, pp. 75-84.
Shen, X., Yu, H., Buford, J. and Akon, M. (Eds.) (2009). Handbook of
Miloš Kudĕlka, Šárka Zehnalová, Zdenĕk Horák, Pavel Krömer and Václav Snášel
the 9th SIAM International Conference on Data Mining, Sparks, NV, USA, pp. 1111-1122.
Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R. and Kadie, C. (2001). Dependency networks for inference, collaborative filtering, and datavisualization, The Journal of Machine Learning Research 1: 49-75.
Kahanda, I. and Neville, J. (2009). Using transactional information to predict link strength in online social networks, Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM), San Jose, CA, USA, pp. 74
Daria Panek, Andrzej Skalski, Janusz Gajda and Ryszard Tadeusiewicz
applications, in A.N. Gorban et al. (Eds.), Principal Manifolds for DataVisualization and Dimension Reduction, Springer, Berlin/Heidelberg, pp. 44-67.
Scholz, M. and Vigário, R. (2002). Nonlinear PCA: A new hierarchical approach, 10th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pp. 439-444.
Skalski, A., Zielinski, T. and Deliyski, D. (2008). Analysis of vocal folds movement in high speed videoendoscopy based on level set segmentation and image registration, International Conference on Signals and Electronic
Žilinskas, J. (2013). Multidimensional DataVisualization: Methods and Applications , Optimization and Its Applications, Vol. 75, Springer-Verlag, New York, NY.
Einbeck, J. and Kalantan, Z. (2013). Intrinsic dimensionality estimation for high-dimensional data sets: New approaches for the computation of correlation dimension, Journal of Emerging Technologies in Web Intelligence 5 (2): 91–97.
Elgammal, A. and su Lee, C. (2004a). Inferring 3d body pose from silhouettes using activity manifold learning, IEEE Computer Society Conference on Computer Vision and