Fuzzy clustering with hedge algebra

Authors:
Dinh Khac Dong;Tran Dinh Khang;Phan Anh Phong
Affiliations:
Hanoi University of Technology, Hanoi, Vietnam;Hanoi University of Technology, Hanoi, Vietnam;Vinh University, Nghean, Vietnam
Venue:
Proceedings of the 2010 Symposium on Information and Communication Technology
Year:
2010

Citing 5
Cited 0

Hedge algebras: an algebraic approach to structure of sets of linguistic truth values

Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Hedge Algebra Based Type-2 Fuzzy Logic System and its Application to Predict Survival Time of Myeloma Patients

KSE '09 Proceedings of the 2009 International Conference on Knowledge and Systems Engineering
Fuzzy clustering with volume prototypes and adaptive cluster merging

IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a new approach to fuzzy clustering in order to handle the uncertainties in pattern recognition problems on the basis of conventional fuzzy C-means algorithm (FCM). In our approach, we define the concept of linguistic cluster center by employing the semantic structure of hedge algebra. This kind of cluster center is constructed to give the appropriate weights for each pattern of the dataset in our clustering algorithm. The parameters of hedge algbra are then optimized in the training process to obtain the suitable parameters for the dataset. We also incorporate the k-means algorithm to get better results in comparing to conventional FCM.