Hierarchical hesitant fuzzy K-means clustering algorithm
- 469 Downloads
Due to the limitation and hesitation in one’s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.
KeywordsHesitant fuzzy set hierarchical clustering K-means clustering intuitionisitc fuzzy set
MR Subject Classification90B50 68T10 62H30
Unable to display preview. Download preview PDF.
- D F Chen, Y J Lei, Y Tian. Clustering algorithm based on intuitionistic fuzzy equivalent relations, J Air Force Eng Univ, 2007, 8: 63–66.Google Scholar
- J Han, M Kamber. Data Mining: Concepts and Techniques, Morgan Kaufmann, San Mateo, CA, 2000.Google Scholar
- M Meil, D Heckerman. An experimental comparison of several clustering and initialization methods, In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1998, 386–395.Google Scholar
- V Torra, Y Narukawa. On hesitant fuzzy sets and decision, the 18th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009, 1378–1382.Google Scholar
- V Torra, S Miyamoto. On the consistency of a fuzzy C-means algorithm for multisets, Artif Intell Res Dev, IOS Press, 2005, 289–295.Google Scholar
- Z S Xu. Intuitionistic fuzzy hierarchical clustering algorithms, J Syst Eng Electron, 2009, 20: 1–5.Google Scholar
- Z S Xu, J J Wu. Intuitionistic fuzzy c-means clustering algorithms, J Syst Eng Electron, 2010, 21: 580–590.Google Scholar