Structural Clustering and Visualization for Multi-Objective Decision Making
نویسنده
چکیده
Clustering is an important problem in knowledge discovery and decision making. In this study, a multi-objective genetic algorithm (MOGA) is used to search for well separated clusters and each evolved solution is evaluated by both data-driven and human-driven metrics developed for this study. The proposed system in this paper also allows the decision maker to navigate non-dominated solutions and to choose one of them as the final solution. Experimental results on both synthetic and real data sets show promise in finding non-dominated solutions while exploring more promising objective space given the same amount of computational time.
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