Importance–Performance Analysis by Fuzzy C-Means Algorithm

作者：

Highlights：

• We propose a method of fuzzy clustering in Importance–Performance Analysis (IPA).

• The fuzzy partition of a set of attributes is obtained by Fuzzy C-Means Algorithm.

• The results are more suitable for deriving managerial decisions than by the traditional IPA.

• We exemplify and compare the results with those obtained by the traditional IPA.

摘要

•We propose a method of fuzzy clustering in Importance–Performance Analysis (IPA).•The fuzzy partition of a set of attributes is obtained by Fuzzy C-Means Algorithm.•The results are more suitable for deriving managerial decisions than by the traditional IPA.•We exemplify and compare the results with those obtained by the traditional IPA.

论文关键词：Importance–Performance Analysis,Fuzzy clustering,Fuzzy partition,Prototype,Fuzzy C-Means Algorithm

论文评审过程：Received 14 July 2015, Revised 17 November 2015, Accepted 17 December 2015, Available online 25 December 2015, Version of Record 7 January 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.023