A Novel Linguistic Relational Fuzzy C-Means
In real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are sever...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11003957/ |
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| author | Peerawich Phaknonkul Sansanee Auephanwiriyakul Nipon Theera-Umpon |
| author_facet | Peerawich Phaknonkul Sansanee Auephanwiriyakul Nipon Theera-Umpon |
| author_sort | Peerawich Phaknonkul |
| collection | DOAJ |
| description | In real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are several types of clustering including clustering on information from objects themselves or clustering on a relational data. Currently, there are few researches on a fuzzy data clustering and a fuzzy relational data clustering. However, those algorithms are either defuzzifying the data or the relation of the data before implementing the clustering Therefore, in this paper, we develop a linguistic relational fuzzy C-means (LRFCM) that works with fuzzy relation of vectors of fuzzy numbers directly. The algorithm is an extension of the regular relational fuzzy C-means (RFCM). The proposed algorithm does not defuzzify fuzzy attributes beforehand. We found that the results from the LRFCM are similar to that from the RFCM. However, the results from the LRFCM can provide information that contains all the uncertainties from the inputs while the RFCM cannot. We also compare the results with the fuzzy C-means clustering. These comparisons show that the LRFCM behaves the same way as the RFCM and the FCM in partitioning the input space into clusters. |
| format | Article |
| id | doaj-art-593060a8d2144b488b969ddbbccb9b19 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-593060a8d2144b488b969ddbbccb9b192025-08-20T01:56:48ZengIEEEIEEE Access2169-35362025-01-0113893328934210.1109/ACCESS.2025.357001411003957A Novel Linguistic Relational Fuzzy C-MeansPeerawich Phaknonkul0https://orcid.org/0009-0004-4979-9180Sansanee Auephanwiriyakul1https://orcid.org/0000-0002-6639-7165Nipon Theera-Umpon2Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandBiomedical Engineering and Innovation Research Center, Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, ThailandIn real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are several types of clustering including clustering on information from objects themselves or clustering on a relational data. Currently, there are few researches on a fuzzy data clustering and a fuzzy relational data clustering. However, those algorithms are either defuzzifying the data or the relation of the data before implementing the clustering Therefore, in this paper, we develop a linguistic relational fuzzy C-means (LRFCM) that works with fuzzy relation of vectors of fuzzy numbers directly. The algorithm is an extension of the regular relational fuzzy C-means (RFCM). The proposed algorithm does not defuzzify fuzzy attributes beforehand. We found that the results from the LRFCM are similar to that from the RFCM. However, the results from the LRFCM can provide information that contains all the uncertainties from the inputs while the RFCM cannot. We also compare the results with the fuzzy C-means clustering. These comparisons show that the LRFCM behaves the same way as the RFCM and the FCM in partitioning the input space into clusters.https://ieeexplore.ieee.org/document/11003957/Relational fuzzy C-meanslinguistic algorithmlinguistic vectortype-2 fuzzy vectorlinguistic clustering |
| spellingShingle | Peerawich Phaknonkul Sansanee Auephanwiriyakul Nipon Theera-Umpon A Novel Linguistic Relational Fuzzy C-Means IEEE Access Relational fuzzy C-means linguistic algorithm linguistic vector type-2 fuzzy vector linguistic clustering |
| title | A Novel Linguistic Relational Fuzzy C-Means |
| title_full | A Novel Linguistic Relational Fuzzy C-Means |
| title_fullStr | A Novel Linguistic Relational Fuzzy C-Means |
| title_full_unstemmed | A Novel Linguistic Relational Fuzzy C-Means |
| title_short | A Novel Linguistic Relational Fuzzy C-Means |
| title_sort | novel linguistic relational fuzzy c means |
| topic | Relational fuzzy C-means linguistic algorithm linguistic vector type-2 fuzzy vector linguistic clustering |
| url | https://ieeexplore.ieee.org/document/11003957/ |
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