An interval weighed fuzzy c-means clustering by genetically guided alternating optimization
作者：
Highlights：
• Interval attribute weights are proposed and introduced for fuzzy clustering.
• Genetic mechanism and gradient-based iteration constitute optimization strategy.
• Data partition and weights can be obtained by minimizing the objective function.
• Reasonable clustering results can be achieved more easily.
摘要
•Interval attribute weights are proposed and introduced for fuzzy clustering.•Genetic mechanism and gradient-based iteration constitute optimization strategy.•Data partition and weights can be obtained by minimizing the objective function.•Reasonable clustering results can be achieved more easily.
论文关键词：Fuzzy clustering,Attribute weighting,Interval number,Genetic algorithm,Alternating optimization
论文评审过程：Available online 13 April 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.03.042
