Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering
Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster ass...
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| Vydáno v: | Journal of physics. Conference series Ročník 2791; číslo 1; s. 12080 - 12090 |
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| Jazyk: | angličtina |
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Bristol
IOP Publishing
01.07.2024
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset. |
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| AbstractList | Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset. |
| Author | Liu, Wei Yan, Chun Gong, Zheng |
| Author_xml | – sequence: 1 givenname: Zheng surname: Gong fullname: Gong, Zheng organization: Shandong University of Science and Technology Mathematics and System Science Institute, Qingdao 266590, Shandong, China – sequence: 2 givenname: Chun surname: Yan fullname: Yan, Chun organization: Shandong University of Science and Technology Mathematics and System Science Institute, Qingdao 266590, Shandong, China – sequence: 3 givenname: Wei surname: Liu fullname: Liu, Wei organization: Computer Science Institute , Shandong University of Science and Technology, Qingdao 266590, Shandong, China |
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| Cites_doi | 10.22105/BDCV.2021.142085 10.1016/S0169-7439(01)00131-9 |
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| DOI | 10.1088/1742-6596/2791/1/012080 |
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| References_xml | – volume: 46 start-page: 7 year: 2010 ident: JPCS_2791_1_012080bib7 publication-title: Adaptive rough K-Means clustering algorithm. Computer Engineering and Applications – volume: 17 start-page: 520 year: 2001 ident: JPCS_2791_1_012080bib13 publication-title: Missing value estimation methods for DNA microarrays. Bioinformatics – volume: 1 start-page: 96 year: 2021 ident: JPCS_2791_1_012080bib6 publication-title: Rough set theory and its extensions for attribute reduction: a review. Big Data and Computing Visions doi: 10.22105/BDCV.2021.142085 – volume: 58 start-page: 15 year: 2001 ident: JPCS_2791_1_012080bib1 publication-title: Dealing with missing data. Chemometrics and Intelligent Laboratory Systems doi: 10.1016/S0169-7439(01)00131-9 – volume: 11 start-page: 287 year: 2010 ident: JPCS_2791_1_012080bib14 publication-title: Spectral regularization algorithms for learning large incomplete matrices. The Journal of Machine Learning Research – volume: 93 start-page: 230 year: 2021 ident: JPCS_2791_1_012080bib3 publication-title: Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering. Computers and Electrical Engineering – volume: 43 start-page: 614 year: 2015 ident: JPCS_2791_1_012080bib4 publication-title: Missing data imputation by K nearest neighbors based on grey relational structure and mutual information. Applied Intelligence – volume: 6 start-page: 983 year: 2018 ident: JPCS_2791_1_012080bib11 publication-title: MIAEC: Missing data imputation based on the evidence chain. IEEE Access – volume: 32 start-page: 10 year: 2020 ident: JPCS_2791_1_012080bib2 publication-title: A novel fuzzy rough clustering parameter-based missing value imputation. Neural Computing and Applications – start-page: 1 year: 2014 ident: JPCS_2791_1_012080bib8 – volume: 30 start-page: 1 year: 2021 ident: JPCS_2791_1_012080bib5 publication-title: Hybrid missing value imputation algorithms by using fuzzy c-means and vaguely quantified rough sets. IEEE Transactions on Fuzzy Systems – volume: 24 start-page: 4 year: 2020 ident: JPCS_2791_1_012080bib9 publication-title: Missing value imputation using unsupervised machine learning techniques. Soft Computing – volume: 55 start-page: 111 year: 2012 ident: JPCS_2791_1_012080bib12 publication-title: Exact matrix completion via convex optimization. Communications of the ACM – volume: 16 start-page: 197 year: 2016 ident: JPCS_2791_1_012080bib10 publication-title: Nearest neighbor imputation algorithms: a critical evaluation. BMC Medical Informatics and Decision Making |
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| SubjectTerms | Algorithms Clustering Clusters Data analysis Datasets Impact analysis Machine learning Missing data |
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| Title | Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering |
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