Automatic clustering by multi-objective genetic algorithm with numeric and categorical features
作者：
Highlights：
• We have developed a clustering algorithm for an unknown number of clusters by MOGA.
• It works with continuous and categorical featured data sets.
• It can work with data sets having missing values.
• The final solution is selected by majority vote by all non-dominated solutions.
• Context-sensitive and cluster-orient genetic operators are designed.
摘要
•We have developed a clustering algorithm for an unknown number of clusters by MOGA.•It works with continuous and categorical featured data sets.•It can work with data sets having missing values.•The final solution is selected by majority vote by all non-dominated solutions.•Context-sensitive and cluster-orient genetic operators are designed.
论文关键词：Automatic clustering,Multi-Objective Genetic Algorithm (MOGA),Pareto approach,Statistical test
论文评审过程：Received 14 May 2018, Revised 25 May 2019, Accepted 25 June 2019, Available online 26 June 2019, Version of Record 12 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.056
