An effective imputation approach for handling missing data using intuitionistic fuzzy clustering algorithms
It is imperative to handle missing data attentively in the preprocessing stage as it may affects the integrity and quality of real-world datasets. However, existing soft clustering-based imputation neglect the underlying non-spherical separability of the data in feature space. This study proposes tw...
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| Published in | Discover Computing Vol. 28; no. 1; pp. 133 - 29 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.07.2025
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2948-2992 1386-4564 2948-2992 1573-7659 |
| DOI | 10.1007/s10791-025-09639-6 |
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| Abstract | It is imperative to handle missing data attentively in the preprocessing stage as it may affects the integrity and quality of real-world datasets. However, existing soft clustering-based imputation neglect the underlying non-spherical separability of the data in feature space. This study proposes two robust missing data imputation (MDI) algorithms: Linear Interpolation-based Iterative Intuitionistic Fuzzy C-Means with Euclidean distance (LI-IIFCM) and its weighted variant LI-IIFCM-σ. LI-IIFCM and LI-IIFCM-σ uses linear interpolation for initial imputation followed by iterative IFCM and IFCM-σ, respectively. The approach leverages the soft Davies–Bouldin index to determine the optimal number of clusters and then iteratively refines imputations by minimizing average variation. Experimental analysis and statistical analysis (Friedman Test) on four UCI datasets, using two performance metrics, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrate that the proposed algorithms consistently outperform eight existing fuzzy clustering-based MDI algorithms. |
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| AbstractList | It is imperative to handle missing data attentively in the preprocessing stage as it may affects the integrity and quality of real-world datasets. However, existing soft clustering-based imputation neglect the underlying non-spherical separability of the data in feature space. This study proposes two robust missing data imputation (MDI) algorithms: Linear Interpolation-based Iterative Intuitionistic Fuzzy C-Means with Euclidean distance (LI-IIFCM) and its weighted variant LI-IIFCM-σ. LI-IIFCM and LI-IIFCM-σ uses linear interpolation for initial imputation followed by iterative IFCM and IFCM-σ, respectively. The approach leverages the soft Davies–Bouldin index to determine the optimal number of clusters and then iteratively refines imputations by minimizing average variation. Experimental analysis and statistical analysis (Friedman Test) on four UCI datasets, using two performance metrics, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrate that the proposed algorithms consistently outperform eight existing fuzzy clustering-based MDI algorithms. Abstract It is imperative to handle missing data attentively in the preprocessing stage as it may affects the integrity and quality of real-world datasets. However, existing soft clustering-based imputation neglect the underlying non-spherical separability of the data in feature space. This study proposes two robust missing data imputation (MDI) algorithms: Linear Interpolation-based Iterative Intuitionistic Fuzzy C-Means with Euclidean distance (LI-IIFCM) and its weighted variant LI-IIFCM-σ. LI-IIFCM and LI-IIFCM-σ uses linear interpolation for initial imputation followed by iterative IFCM and IFCM-σ, respectively. The approach leverages the soft Davies–Bouldin index to determine the optimal number of clusters and then iteratively refines imputations by minimizing average variation. Experimental analysis and statistical analysis (Friedman Test) on four UCI datasets, using two performance metrics, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrate that the proposed algorithms consistently outperform eight existing fuzzy clustering-based MDI algorithms. |
| ArticleNumber | 133 |
| Author | Gosain, Anjana Singh, Jaspreeti Sethia, Kavita |
| Author_xml | – sequence: 1 givenname: Kavita surname: Sethia fullname: Sethia, Kavita email: kavita.17316490020@ipu.ac.in organization: USICT, Guru Gobind Singh Indraprastha University – sequence: 2 givenname: Jaspreeti surname: Singh fullname: Singh, Jaspreeti organization: USICT, Guru Gobind Singh Indraprastha University – sequence: 3 givenname: Anjana surname: Gosain fullname: Gosain, Anjana organization: USICT, Guru Gobind Singh Indraprastha University |
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| SubjectTerms | Algorithms Clustering Computer Science Data Mining and Knowledge Discovery Data Structures and Information Theory Datasets Decision trees Euclidean geometry FCM Fuzzy clustering IFCM Information Storage and Retrieval Interpolation Intuitionistic fuzzy C-means Linear interpolation Literature reviews Machine learning Missing data Natural Language Processing (NLP) Pattern Recognition Performance measurement Root-mean-square errors Set theory Statistical analysis Statistical methods Support vector machines |
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| Title | An effective imputation approach for handling missing data using intuitionistic fuzzy clustering algorithms |
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