Improvements to the relational fuzzy c-means clustering algorithm

作者：

Highlights：

• Improved relational fuzzy c-means for clustering relational data D is proposed.

• The matrix D is transformed to Euclidean matrix D˜ using different transformations.

• Quality of D˜ is judged by the ability of RFCM to discover the apparent clusters.

• The subdominant ultrametric transformation produces much better partitions of D˜.

• β-spread minimizes the distortion between D and D˜, but produces worst clusterings.

摘要

•Improved relational fuzzy c-means for clustering relational data D is proposed.•The matrix D is transformed to Euclidean matrix D˜ using different transformations.•Quality of D˜ is judged by the ability of RFCM to discover the apparent clusters.•The subdominant ultrametric transformation produces much better partitions of D˜.•β-spread minimizes the distortion between D and D˜, but produces worst clusterings.

论文关键词：Fuzzy clustering,Relational c-means,Euclidean distance matrices

论文评审过程：Received 30 December 2013, Revised 20 June 2014, Accepted 23 June 2014, Available online 8 July 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.06.021