$k$-POD: A Method for $k$-Means Clustering of Missing Data

Abstract

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.

26 pages, 7 tables

References in corpus (3)

• Natural Scales in Geographical Patterns
• Proceedings of the 29th International Conference on Machine Learning (ICML-12)
• Revisiting k-means: New Algorithms via Bayesian Nonparametrics

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• Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models
• Optimal Clustering with Missing Values
• Clustering multilayer graphs with missing nodes
• Clustering with missing data: which imputation model for which cluster analysis method?