A new iterative fuzzy clustering algorithm for multiple imputation of missing data
Sanaz Nikfalazar, Chung‐Hsing Yeh, Susan Bedingfield, Hadi Akbarzadeh Khorshidi · 2017
This paper proposes a new iterative fuzzy clustering (IFC) algorithm to impute missing values of datasets. The information provided by fuzzy clustering is used to update the imputed values through iterations. The performance of the IFC algorithm is examined by conducting experiments on three commonly used datasets and a case study on a city mobility database. Experimental results show that the IFC algorithm not only works well for datasets with a small number of missing values but also provides an effective imputation result for datasets where the proportion of missing data is high.