Improved k-means clustering based on Efros distance for longitudinal data

The traditional k-means algorithm is often calculated according to the Euclidean distance. For longitudinal data it is unable to perform accurate and efficient computing. Based on extended Frobenius-norm (Efros) distance, in this study we proposed a method to improve the selection of initial centers for k-means clustering. This method can improve the traditional k-means clustering on longitudinal data. For missing longitudinal data, we first adopted a linear interpolation strategy to fill in missing values and then standardized the data, etc. Through comprehensive simulation studies, we demonstrate the power and effectiveness of our method by comparing the similarity within and between the classes. The results of our experiments show that our method can cluster the longitudinal data more effectively.