Optimized Multi-Algorithm Voting: Increasing objectivity in clustering
作者：
Highlights：
• Depending on clustering algorithm used, the results may change.
• Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved.
• OMAV is developed that uses an optimization algorithm as integrative method.
• OMAV is applied to the example of country clustering using GLOBE data.
• Increased robustness and reduced subjectivity are demonstrated.
摘要
•Depending on clustering algorithm used, the results may change.•Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved.•OMAV is developed that uses an optimization algorithm as integrative method.•OMAV is applied to the example of country clustering using GLOBE data.•Increased robustness and reduced subjectivity are demonstrated.
论文关键词：Clustering,Integrative methods,Multi-algorithm voting,Work-related values
论文评审过程：Received 28 June 2017, Revised 21 September 2018, Accepted 23 September 2018, Available online 24 September 2018, Version of Record 13 October 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.047
