Truong, H.Q. and Ngo, L.T. and Pedrycz, W. (2017) Advanced Fuzzy Possibilistic C-means Clustering based on Granular Computing. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, 9 October 2016 through 12 October 2016.
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Abstract
With the rapid development of uncertain and large-scale datasets, Fuzzy Possibilistic C-means Clustering (FPCM) and Granular Computing (GrC) were introduced together with the aim to solve the feature selection and outlier detection problems. Utilizing the advantages of the FPCM and GrC, an Advanced Fuzzy Possibilistic C-means Clustering based on Granular Computing (GrFPCM) was proposed to select features as a preprocessing step for clustering problems and granular space is used to handle the uncertainty. Experimental results reported for various datasets in comparison with other approaches exhibit the advantages of the proposed method. © 2016 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Divisions: | Faculties > Faculty of Information Technology Faculties > Faculty of Information Technology |
Identification Number: | 10.1109/SMC.2016.7844627 |
Uncontrolled Keywords: | Cybernetics; Data handling; Fuzzy clustering; Granular computing; Statistics; Clustering problems; Fuzzy possibilistic c-means; Large-scale datasets; Outlier Detection; Pre-processing step; Feature extraction |
Additional Information: | Conference code: 126403. Language of original document: English. |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/9736 |