Toolbox for Distance Estimation and Cluster Validation on Data With Missing Values
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DOI: 10.1109/access.2021.3136435
Abstract: Missing data are unavoidable in the real-world application of unsupervised machine learning, and their nonoptimal processing may decrease the quality of data-driven models. Imputation is a common remedy for missing values, but directly estimating expected… read more here.
Keywords: missing values; cluster validation; distance estimation;