Dynamic rule graph drawing by genetic search

The recent importance given to the integration of the user in a KDD (knowledge discovery in databases) process, which gives him the opportunity to direct his mining towards his own specific needs, requires the development of new highly interactive visualization tools. For graph based representation of discovered knowledge, this means that layout algorithms must dynamically take modifications into account. The authors present a genetic approach to draw a series of layered directed graphs which model relationships between association rules. We develop new problem-specific genetic operators and show that genetic algorithms are well-adapted to solve a multiobjective problem: meeting static aesthetic requirements such as minimizing arc crosses and preserving the “user´s mental map” when a transformation is interactively performed on the graph. Experimental results are presented on several randomly generated series of graphs