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arXiv · 1411.7013

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

Jocelyn T. Chi·Eric C. Chi·Richard G. Baraniuk
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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.

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Jocelyn T. Chi, Eric C. Chi, Richard G. Baraniuk. 2014-11-25. $k$-POD: A Method for $k$-Means Clustering of Missing Data. https://doi.org/10.1080/00031305.2015.1086685

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