Affiliations: Knowledge Engineering and Machine Learning group, Department Statistics and Operation Research, Universitat Politècnica de Catalunya–BarcelonaTech, Barcelona, Spain. E-mail: karina.gibert@upc.edu | Department of Operations Research, Universidad de Los Andes, Mérida, Venezuela | Instituto de Matemática, Estadistica e Computacao Cientifica, Departamento de Matemática Aplicada, U. Estadual de Campinas, Sao Paulo, Brasil
Abstract: Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions – tone and saturation – representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions.
