Evolutionary algorithm based pattern discovery in graphical databases

The greedy search is often used in some existing prevalent graphical data mining systems which often ends up with sub-optimal solutions. To overcome its limits, an evolutionary algorithms based system is developed to perform data mining on databases represented as graphs. New operators of mutation and crossover on graphical databases are defined, and the way of collecting instances of a certain substructure is improved. In addition, a variant of hill-climbing is integrated into the design of mutation operator to improve the capability of local search of evolutionary algorithm. Experimental results show that these measures successfully improve the searching capability of the algorithm and the qualities of solutions.