Efficient Clustering Method Based on Rough Set and Genetic Algorithm
Jianyong Chen, Changsheng Zhang · Procedia Engineering · 2011
In the process of traditional hard clustering, the obtained data objects in clusters are certain. However, the objects in different classes do not have clear boundaries between in reality. A method of dealing with uncertain boundary objects is provided by Rough set theory. Therefore, combing two methods of rough set theory and k-means cluster the objects. At the same time, though the traditional k-means algorithm has powerful local search capability, it easily falls into local optimum. The genetic algorithm can get the global optimal solution, but its convergence is fast. So in the process of clustering, rough set theory and genetic algorithm are introduced. An efficient clustering method based on rough set theory and genetic algorithm is provided. Finally, the experimental results show that the proposed algorithm has the ability to adjust the results and obtain the higher accuracy rate.