Nonlinear multicriteria clustering based on multiple dissimilarity matrices
作者：
Highlights：
• A new hard clustering multicriteria algorithm is presented.
• The algorithm is based on a more general nonlinear aggregation criterion.
• Relevance weights for dissimilarity matrices may be local (in each group) or global.
• In order to optimize the nonlinear criterion, linear programming is used.
• The linear programming approach allows the introduction of some type of constraints.
摘要
Highlights•A new hard clustering multicriteria algorithm is presented.•The algorithm is based on a more general nonlinear aggregation criterion.•Relevance weights for dissimilarity matrices may be local (in each group) or global.•In order to optimize the nonlinear criterion, linear programming is used.•The linear programming approach allows the introduction of some type of constraints.
论文关键词：Clustering analysis,Relational data,Multicriteria decision support,Nonlinear optimization
论文评审过程：Received 30 November 2012, Revised 24 May 2013, Accepted 10 June 2013, Available online 19 June 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.06.008
