A fuzzy rule-based classification system using Hedge Algebraic Type-2 Fuzzy Sets

Phong, Phan Anh; Khang, Trần Đình; Dong, Dinh Khac

doi:10.1109/nafips.2016.7851623

2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) 2016

DOI: 10.1109/nafips.2016.7851623

|View full text |Cite

A fuzzy rule-based classification system using Hedge Algebraic Type-2 Fuzzy Sets

Phan Anh Phong

Trần Đình Khang

Dinh Khac Dong³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value m i represents the fuzzy parameter, which corresponds to the type-2 fuzzy set membership representation of the element X i in the clusters. The next step for this research is to apply hedge algebraic type-2 fuzzy sets [15][16][17] to determine the parameter m i .…”

mentioning

confidence: 99%

Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

et al. 2020

View full text Add to dashboard Cite

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

show abstract

mentioning

confidence: 99%

Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

et al. 2020

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A fuzzy rule-based classification system using Hedge Algebraic Type-2 Fuzzy Sets

Cited by 1 publication

References 8 publications

Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Contact Info

Product

Resources

About