A Novel Fuzzy Rough Clustering Parameter-based missing value imputation

For a long time, missing values are the biggest challenging problem in data mining, machine learning and statistical analysis. In the current scenario, various methods exist to handle the missing values as it's an important task to discover meaningful information. However, the most frequently used method to handle the missing values in a large dataset is discarding the instances with missing values. In such situation, deletion of instances with missing values causes loss of crucial information, which affects the performance of algorithms. Hence, an intelligent method needs to handle the missing values. In the recent past, the fuzzy and rough set has been widely employed in many applications. In this research work, a Novel Fuzzy C-Means Rough Parameter-based missing value imputation method is proposed with the hybridization of the fuzzy and rough set to handle missing values. The proposed algorithm is capable of handling the situation of uncertainty and vagueness in datasets through rough and fuzzy sets while maintaining vital information. The experimentation has been carried out on three benchmark datasets such as the Dukes' B colon cancer dataset, the Mice Protein Expression and Yeast datasets to asses the efficacy of the proposed method. It is observed that the proposed method produces improved results than Fuzzy C-Means Centroid-based missing value imputation and Fuzzy C-Means Parameter-based missing value imputation method.