The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Web-scale k-means clustering
Abstract
We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast ε-accurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml
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A framework for feature selection in clustering
Witten, D. M., & Tibshirani, R. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2010.tm09415 · Cited by 696
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Metadata
- Authors
- Sculley, D.
- Venue
- Proceedings of the 19th international conference on World wide web
- Publisher
- ACM
- Published
- 2010-04-26
- Pages
- 1177-1178
- OpenAlex
- W2142838865
- Cited by
- 1,147
- References
- 1
- FWCI
- 6.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
