Cluster analysis based in fuzzy relations
Fuzzy Sets and Systems - Special issue on clustering and learning
Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory
Computers & Mathematics with Applications
Establishing performance evaluation structures by fuzzy relation-based cluster analysis
Computers & Mathematics with Applications
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The reasonalbe and effective determination of the weight allocation is very critical to multi-attribute decision-making. This paper presents a novel multi-attribute weight allocation method based on the fuzzy clustering analysis and the information entropy theory in rough sets theory. It first studies the fuzzy clustering analysis method based on fuzzy transitive closure with the introduction of the information entropy theory in rough sets. Furthermore, it discusses the detailed steps of the proposed approach thoroughly . After the fuzzy clustering of the source data, the overall reasonable threshold is extracted based on F-statistics and the multi-attribute weight allocation is obtained using the information entropy theory. Finally, a case study is given to show the reasonability and validity of the proposed approach.