Soft Clustering Based Missing Value Imputation
P. S. Raja, K. Thangavel · Communications in computer and information science · 2016
Preprocessing is one of the steps in Data Mining, which involves Noise removal, Identification of outlier, Normalization, Data transformation, Handling missing values, etc. Missing value is a common problem in large datasets. Most frequently used method to handle missing values by statistical is discarding the instances with missing values. Sometime deletion of instances with missing values cause loss of essential information, which affects the performance of statistical and machine learning algorithms. This paper focuses on handling missing values using unsupervised learning techniques. Rough K-Means based missing value imputation was proposed and compared with K-Means, Fuzzy C-Means based imputation methods. The experimental analysis is carried out on two data sets Lung Cancer and Cleveland Heart data sets. The proposed method achieves the best accuracy for some of the datasets.