Dang, T.H. and Ngo, L.T. and Pedrycz, W. (2015) Interval Type-2 fuzzy C-Means approach to collaborative clustering. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, 2 August 2015 through 5 August 2015.
Interval Type-2 fuzzy C-Means approach to collaborative clustering.pdf
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Abstract
There have been numerous studies on using the FCM algorithm in clustering and collaboration clustering, especially in data analysis, data mining and pattern recognition. In this study, we present new methods involving interval Type-2 fuzzy sets to realize collaborative clustering. Data in which the clustering results realized at one data site impact clustering carried out at other data sites. Those methods endowed with interval type-2 fuzzy sets help cope with uncertainties present in data. The experiment with weather data sets has shown better results in comparison with the previous approaches. © 2015 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Divisions: | Faculties > Faculty of Information Technology Institutes > Institute of Simulation Technology |
Identification Number: | 10.1109/FUZZ-IEEE.2015.7337932 |
Uncontrolled Keywords: | Algorithms; Clustering algorithms; Data mining; Fuzzy clustering; Fuzzy sets; Fuzzy systems; Pattern recognition; Cluster validity measures; Clustering results; Collaborative clustering; FCM algorithm; Fuzzy C mean; Interval type-2 fuzzy; Interval type-2 fuzzy sets; Type-2 fuzzy set; Cluster analysis |
Additional Information: | Conference code: 118450. Language of original document: English. |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/9895 |