Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function
Fuzzy C-Means (FCM) is one of the mostly used techniques for fuzzy clustering and proven to be robust and more efficient based on various applications. Image segmentation, stock market and web analytics are examples of popular applications which use FCM. One limitation of FCM is that it only produce...
| Main Authors: | Khairuddin, S.H., Hasan, M.H., Hashmani, M.A. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30043 / |
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Horizon Research Publishing
2020
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093905559&doi=10.13189%2fms.2020.080509&partnerID=40&md5=83794fa2a3e199a709a8e113e7234269 http://eprints.utp.edu.my/30043/ |
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utp-eprints.300432022-03-25T03:22:02Z Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function Khairuddin, S.H. Hasan, M.H. Hashmani, M.A. Fuzzy C-Means (FCM) is one of the mostly used techniques for fuzzy clustering and proven to be robust and more efficient based on various applications. Image segmentation, stock market and web analytics are examples of popular applications which use FCM. One limitation of FCM is that it only produces Gaussian membership function (MF). The literature shows that different types of membership functions may perform better than other types based on the data used. This means that, by only having Gaussian membership function as an option, it limits the capability of fuzzy systems to produce accurate outcomes. Hence, this paper presents a method to generate another popular shape of MF, the trapezoidal shape (trapMF) from FCM to allow more flexibility to FCM in producing outputs. The construction of trapMF is using mathematical theory of Gaussian distributions, confidence interval and inflection points. The cluster centers or mean (μ) and standard deviation (�) from the Gaussian output are fully used to determine four trapezoidal parameters; lower limit a, upper limit d, lower support limit b, and upper support limit c with the assistance of function trapmf() in Matlab fuzzy toolbox. The result shows that the mathematical theory of Gaussian distributions can be applied to generate trapMF from FCM. © 2020 by authors, all rights reserved. Horizon Research Publishing 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093905559&doi=10.13189%2fms.2020.080509&partnerID=40&md5=83794fa2a3e199a709a8e113e7234269 Khairuddin, S.H. and Hasan, M.H. and Hashmani, M.A. (2020) Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function. Mathematics and Statistics, 8 (5). pp. 559-565. http://eprints.utp.edu.my/30043/ |
| institution |
Universiti Teknologi Petronas |
| collection |
UTP Institutional Repository |
| description |
Fuzzy C-Means (FCM) is one of the mostly used techniques for fuzzy clustering and proven to be robust and more efficient based on various applications. Image segmentation, stock market and web analytics are examples of popular applications which use FCM. One limitation of FCM is that it only produces Gaussian membership function (MF). The literature shows that different types of membership functions may perform better than other types based on the data used. This means that, by only having Gaussian membership function as an option, it limits the capability of fuzzy systems to produce accurate outcomes. Hence, this paper presents a method to generate another popular shape of MF, the trapezoidal shape (trapMF) from FCM to allow more flexibility to FCM in producing outputs. The construction of trapMF is using mathematical theory of Gaussian distributions, confidence interval and inflection points. The cluster centers or mean (μ) and standard deviation (�) from the Gaussian output are fully used to determine four trapezoidal parameters; lower limit a, upper limit d, lower support limit b, and upper support limit c with the assistance of function trapmf() in Matlab fuzzy toolbox. The result shows that the mathematical theory of Gaussian distributions can be applied to generate trapMF from FCM. © 2020 by authors, all rights reserved. |
| format |
Article |
| author |
Khairuddin, S.H. Hasan, M.H. Hashmani, M.A. |
| spellingShingle |
Khairuddin, S.H. Hasan, M.H. Hashmani, M.A. Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| author_sort |
Khairuddin, S.H. |
| title |
Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| title_short |
Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| title_full |
Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| title_fullStr |
Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| title_full_unstemmed |
Integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| title_sort |
integration of cluster centers and gaussian distributions in fuzzy c-means for the construction of trapezoidal membership function |
| publisher |
Horizon Research Publishing |
| publishDate |
2020 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093905559&doi=10.13189%2fms.2020.080509&partnerID=40&md5=83794fa2a3e199a709a8e113e7234269 http://eprints.utp.edu.my/30043/ |
| _version_ |
1741197340641853440 |
| score |
11.62408 |