A new fuzzy clustering-based imputation method
Fuzzy clustering has been used in numerous research disciplines and commercial applications to identify groups of real-world objects. Most fuzzy clustering algorithms require complete datasets; however, real-world datasets may have missing values due to technical limitations. To address this problem...
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| Định dạng: | Conference Paper |
| Ngôn ngữ: | English |
| Thông tin xuất bản: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Truy cập trực tuyến: | http://digital.lib.ueh.edu.vn/handle/UEH/62273 https://doi.org/10.1109/CSCI46756.2018.00265 |
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| Tóm tắt: | Fuzzy clustering has been used in numerous research disciplines and commercial applications to identify groups of real-world objects. Most fuzzy clustering algorithms require complete datasets; however, real-world datasets may have missing values due to technical limitations. To address this problem, we present a new algorithm where data are clustered using the Fuzzy C-Means algorithm, followed by approximating the fuzzy partition by a probabilistic data distribution model which is then used for missing value imputation as well as for defuzzification. Using distribution-based approach, our method is most appropriate for datasets where the data are non-uniform. We show that our method outperforms seven popular imputation algorithms on uniform and non-uniform artificial datasets as well as real datasets with unknown data distribution model. |
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