FRACTIONATION USING K MEANS CLUSTERING
A. Kannammal, Sindhu P, R Santhiya, S. Sujitha, S Yuvetha · International Journal of Engineering Technology and Management Sciences · 2023
The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, is common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.