An Elitist-Based Differential Evolution Algorithm for Multiobjective Clustering

被引:0
作者
Zhang, Mingzhu [1 ]
Cao, Jie [1 ]
机构
[1] Nanjing Univ Finance & Econ, Sch Informat Engn, Nanjing 210023, Peoples R China
来源
2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020) | 2020年
关键词
multiobjective clustering; number of clusters; differential evolution; elitist archive; multiobjective evolutionary optimization; GENETIC ALGORITHM; ENTROPY; NUMBER;
D O I
10.1109/icaibd49809.2020.9137493
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we convert the clustering problem with an unknown number of clusters into a multiobjective optimization problem, and propose a novel elitist-based differential evolution algorithm for multiobjective clustering (EDEMC). It aims to minimize the number of clusters and maximize the compactness within clusters simultaneously, and generates a Pareto-optimal set consisted of multiple clustering solutions for different cluster numbers. These two optimization objectives are essential factors for clustering. EDEMC creates and maintains an elitist archive which stores historical best solutions for each number of cluster, and it iteratively optimizes the population with newly designed genetic operations and replenishment strategy. In the end, users could flexibly choose one optimal partitioning of a certain number of clusters by some preferred criteria from the solution set. Experimental results on several datasets illustrate that the proposed method can provide more convergent and diverse solutions in a shorter time.
引用
收藏
页码:161 / 166
页数:6
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