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arXiv · 1906.00938

Big-Data Clustering: K-Means or K-Indicators?

Feiyu Chen·Yuchen Yang·Liwei Xu·Taiping Zhang·Yin Zhang
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Abstract

The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new algorithm when K is large. In particular, using the new algorithm to start the K-means algorithm, without any replication, can significantly outperform the standard K-means with a large number of currently state-of-the-art random replications.

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Feiyu Chen, Yuchen Yang, Liwei Xu, Taiping Zhang, Yin Zhang. 2019-06-03. Big-Data Clustering: K-Means or K-Indicators?. https://arxiv.org/abs/1906.00938

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