Uncertain fuzzy clustering: Insights and recommendations
- Authors
- Rhee, Frank Chung-Hoon
- Issue Date
- Feb-2007
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, v.2, no.1, pp.44 - 56
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
- Volume
- 2
- Number
- 1
- Start Page
- 44
- End Page
- 56
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/43892
- DOI
- 10.1109/MCI.2007.357193
- ISSN
- 1556-603X
- Abstract
- Interval type-2 fuzzy sets were used to model the uncertainty that is associated with the various parameters in objective function-based clustering. The purpose was to represent and manage the uncertainty in the cluster memberships by incorporating interval type-2 fuzzy sets. As a result, interval type-2 clustering methods were obtained by modifying the prototype-updating and hard-partitioning procedures in the type-1 fuzzy objective function-based clustering. As a consequence, the management of uncertainty by an interval type-2 fuzzy approach aids cluster prototypes to converge to a more desirable location than a type-1 fuzzy approach. Several examples illustrated the effectiveness of interval type-2 fuzzy approach methods. Furthermore, the uncertainty associated with the parameters for other existing clustering algorithms can be considered in the development of several other interval type-2 clustering algorithms. They are currently under investigation.
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