An Adaptive Multiobjective Genetic Algorithm with Fuzzy c -Means for Automatic Data Clustering

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Abstract

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

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Dong, Z., Jia, H., & Liu, M. (2018). An Adaptive Multiobjective Genetic Algorithm with Fuzzy c -Means for Automatic Data Clustering. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/6123874

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