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- W4384135040 abstract "The main objective of any clustering method is to improve the clusters quality. Such an improvement can be achieved using graph partitioning algorithm which partitions the graph into maximum components with minimum cut which represents the optimality of spectrum partitioning. The commonly used graph portioning algorithm is spectral algorithm called known popularly as spectral clustering. Such a unsupervised method can be used during imputation for identifying the optimal clusters. Optimal clusters reduce the search space during imputation and thus achieve dimensionality reduction. The proposed method uses MKNNMBI imputation method in which the non-missing dataset used for imputing the missing values is reduced. The reduction is achieved by using spectral partitioning method for which the non-missing dataset is represented as a graph. The spectrum of a Laplacian graph is obtained using spectral clustering from which the optimal l eigen values are identified as optimal cluster centers for imputation. The imputation is done using this reduced optimal non-missing dataset. The imputed dataset is evaluated by comparing the accuracies of classifiers like SVM, C4.5, NB and kNN. Proposed method has improved the accuracies of the imputation on optimal reduced datasets." @default.
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- W4384135040 date "2023-01-01" @default.
- W4384135040 modified "2023-10-16" @default.
- W4384135040 title "Unsupervised Learning Method for Better Imputation of Missing Values" @default.
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- W4384135040 doi "https://doi.org/10.1007/978-3-031-35644-5_3" @default.
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