An efficient k-means-type algorithm for clustering datasets with incomplete records
The k‐means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under an assumed missing‐completely‐at‐random mechanism or to ignore the incomplete records, and apply the algorithm on the resulting dataset. We develop an efficient version of the k‐means algorithm that allows for clustering in the presence of incomplete records. Our extension is called km‐means and reduces to the k‐means algorithm when all records are complete. We also provide initialization strategies for our algorithm and methods to estimate the number of groups in the dataset. Illustrations and simulations demonstrate the efficacy of our approach in a variety of settings and patterns of missing data. Our methods are also applied to the analysis of activation images obtained from a functional magnetic resonance imaging experiment.
This is the peer-reviewed version of the following article: Lithio, Andrew, and Ranjan Maitra. "An efficient k‐means‐type algorithm for clustering datasets with incomplete records." Statistical Analysis and Data Mining: The ASA Data Science Journal 11, no. 6 (2018): 296-311, which has been published in final form at DOI: 10.1002/sam.11392. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Posted with permission.