Data Clustering Method Using Efficient Fuzzifier Values Derivation
The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the n...
Main Authors: | Jaehyuk Cho, Wonhee Joo |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9127904/ |
Similar Items
-
The Structures of Fuzzifying Measure
by: Shi Hua Luo, et al.
Published: (2014-05-01) -
New types of continuity and openness in fuzzifying bitopological spaces
by: A.A. Allam, et al.
Published: (2016-04-01) -
On Soft Semi-Open Sets and Soft Semi-Continuity in Fuzzifying Soft Topological Spaces
by: Ramadhan A. Mohammed, et al.
Published: (2018-12-01) -
A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation
by: Chengmao Wu, et al.
Published: (2021-01-01) -
A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication
by: Yu-Cheng Wang, et al.
Published: (2021-06-01)