Abstract
This paper describes Traffic Lights Panel (TLP) as a useful interpretation-oriented tool for clustering results, suitable for helping the domain experts to induce a conceptualization of the resulting profiles. Till now, the TLP is manually derived from the clustering results, but it has been well accepted by the domain experts of several real applications as a very helpful contribution to understand the classes’ meaning and improve reliable decision-making. Here, a proposal to automatically construction of TLP is presented trying to mimic the real process that the analyst performs. Two criteria based on different central trend statistics of the variables inside a class are introduced, tested with a real case study in Neurorehabilitation field and compared. Finally, uncertainty concerning TLP is analyzed; the annotated TLP (aTLP) is proposed to visualize uncertainty associated to the decisions derived from TLP, thus enhancing robustness of TLP as a supporting tool in decision-making.
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References
Lezak, M.D.: Neuropsychological Assessment, 4th edn. Oxford University Press, New York (2004)
Gibert, K., et al.: Response to TBI-neurorehabilitation through an AI& Stats hybrid KDD methodology. Medical Archives 62(3), 132–135 (2008)
Fayyad, U.: Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge (1996)
Gibert, K., et al.: Data Mining for Environmental Systems. In: Environmental Modelling, Software and Decision Support. State of the art and New Perspectives. IDEA Series, vol. (3), pp. 205–228. Elsevier (2008)
Gibert, K., Sànchez-Marrè, M.: Outcomes from the iEMSs Data Mining in the Environmental Sciences Workshop Series. Environmental Modelling and Software (26), 983–985 (2011)
Gibert, K., Nonell, R., Velarde, J.M., Colillas, M.M.: Knowledge Discovery with clustering: impact of metrics and reporting phase by using KLASS. Neural Network World (04), 319–326 (2005)
Gibert, K., Rodríguez-Silva, G., Rodríguez-Roda, I.: Knowledge Discovery with Clustering based on rules by States: A water treatment application. Environmental Modelling and Software (25), 712–723 (2010)
Lindsey, J.C., Jacobson, D.L., Li, H., Houseman, E.A., Aldrovandi, G.M., Mulligan, K.: Using Cluster Heat Maps to Investigate Relationships Between Body Composition and Laboratory Measurements in HIV-Infected & HIV-Uninfected Children & Young Adults. J. of Acquired Immune Deficiency Syndromes 59(3), 325–338 (1999)
Siponen, M., Vesanto, J., Simula, O., Vasara, P.: An approach to automated interpretation of SOM. Advances in Self-Organising Maps, 89–94 (2001)
Yan, G., Li, Z.: Using cluster similarity to detect natural cluster hierarchies. In: Proceedings of Fourth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 291–295 (2007)
Barnard, K., Duygulu, P., Forsyth, D.: Clustering art. In: Computer Vision and Pattern Recognition, vol. 2, pp. 434–441 (2001)
Trauwaert, T., Rouseew, P., Kauffman, L.: Some silhouette-based graphics for clustering interpretation. Belgian J. Operations Research, Statistics and Computer Science 29(3), 35–55
Rohling, M.: Effectiveness of Cognitive Rehabilitation Following Acquired Brain Injury: A Meta-Analytic Re-Examination of Cicerone et al.’s (2000, 2005) Systematic Reviews. Neuropsychology 23(1), 20–39 (2009)
ECRI, Cognitive Rehabilitation Therapy for Traumatic Brain Injury: What We Know and Don’t Know about Its Efficacy, EDITORIAL NOTE 10/11/11: IOM.s New Report on Brain Injury Treatments Draws Conclusions Similar to ECRI Institute’s Earlier Findings (2011)
Tormos (ed.): Desarrollo de Herramientas para evaluar el resultado de las tecnologías aplicadas al proceso rehabilitador. Estudio a partir de dos modelos concretos: Lesión Medular y Daño Cerebral Adquirido. Madrid: Plan de Calidad para el Sistema Nacional de Salud. Ministerio de Sanidad y Consumo. Agència d’avaluació de tecnologia i recerca mèdiques de Catalunya. Informes de evaluación de tecnologías sanitarias AATRM, n 2006/12 (2007)
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Gibert, K., Conti, D., Sànchez-Marrè, M. (2012). Decreasing Uncertainty When Interpreting Profiles through the Traffic Lights Panel. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31715-6_16
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DOI: https://doi.org/10.1007/978-3-642-31715-6_16
Publisher Name: Springer, Berlin, Heidelberg
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