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arXiv · 1609.04723

K-Medoids For K-Means Seeding

James Newling·François Fleuret
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Abstract

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better K-medoids solutions than the Voronoi iteration algorithm. This finding, along with the similarity between the Voronoi iteration algorithm and Lloyd's K-means algorithm, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other seeding algorithms on 23/23 datasets with a mean decrease over K-means++ of 30% for initialization mse and 3% or final mse. We describe how the complexity and runtime of clarans can be improved, making it a viable initialization scheme for large datasets.

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James Newling, François Fleuret. 2016-09-15. K-Medoids For K-Means Seeding. https://arxiv.org/abs/1609.04723

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