Big-Data Clustering: K-Means or K-Indicators?
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.
Cite
@article{arxiv.1906.00938,
title = {Big-Data Clustering: K-Means or K-Indicators?},
author = {Feiyu Chen and Yuchen Yang and Liwei Xu and Taiping Zhang and Yin Zhang},
journal= {arXiv preprint arXiv:1906.00938},
year = {2019}
}