fuzzy gravitational search algorithm an approach for data mining

the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major roles on search process of gsa. then the improved gsa (namely fuzzy-gsa) is employed to construct a novel data mining algorithm for classification rule discovery from reference data sets. extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. the comparative results illustrate that performance of the proposed fgsa-miner considerably outperforms the standard gsa. also it is shown that the performance of the fgsa-miner is comparable to, sometimes better than those of the cn2 (a traditional data mining method) and similar approach which have been designed based on other swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and evolutionary algorithm (genetic algorithm).