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A multi-clustering method based on evolutionary multiobjective optimization with grid decomposition
Swarm and Evolutionary Computation ( IF 10 ) Pub Date : 2020-03-31 , DOI: 10.1016/j.swevo.2020.100691
Lisong Wang , Guonan Cui , Qing Zhou , Kui Li

At present, it is a challenging task to determine the number of clusters (k), which has a great impact on the quality of major clustering methods. Multi-objective evolutionary algorithms (MOEAs), which determines k value adaptively, have been widely adopted for clustering. However, when the range of k becomes increasingly large, Pareto Front (PF) approximations obtained by an MOEA may not be uniformly distributed, leading to the difficulty of obtaining the optimal k value for clustering. For this reason, an MOEA base on constrained decomposition with grids (CCDG-K) is designed for clustering and better identify the optimal k value. CCDG-K adopts a grid system for decomposition. The grid system has an inherent property of reserving diversely populated solutions, which is very desirable for clustering. The experimental studies show that CCDG-K can deliver solutions of all k values which is of great help for obtaining the optimal k value. The experimental results also indicate that CCDG-K outperforms other algorithms in clustering.



中文翻译:

基于进化多目标优化与网格分解的多聚类方法

目前,确定聚类数(k)是一项艰巨的任务,这对主要聚类方法的质量有很大影响。自适应地确定k值的多目标进化算法(MOEA)已广泛用于聚类。但是,当k的范围变得越来越大时,由MOEA获得的Pareto Front(PF)近似值可能无法均匀分布,从而导致难以获得用于聚类的最佳k值。因此,设计了基于网格约束分解的MOEA(CCDG-K)进行聚类并更好地确定最优k值。CCDG-K采用网格系统进行分解。网格系统具有保留各种填充解决方案的固有属性,这对于集群非常有用。实验研究表明,CCDG-K可以提供所有k值的解,这对于获得最佳k值有很大帮助。实验结果还表明,CCDG-K在聚类方面优于其他算法。

更新日期:2020-03-31
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