Corpus ID: 16711194

Learning Mixtures of Gaussians using the k-means Algorithm

@article{Chaudhuri2009LearningMO,
  title={Learning Mixtures of Gaussians using the k-means Algorithm},
  author={Kamalika Chaudhuri and Sanjoy Dasgupta and Andrea Vattani},
  journal={ArXiv},
  year={2009},
  volume={abs/0912.0086}
}
  • Kamalika Chaudhuri, Sanjoy Dasgupta, Andrea Vattani
  • Published 2009
  • Mathematics, Computer Science
  • ArXiv
  • One of the most popular algorithms for clustering in Euclidean space is the k-means algorithm; k-means is difficult to analyze mathematically, and few theoretical guarantees are known about it, particularly when the data is well-clustered. In this paper, we attempt to fill this gap in the literature by analyzing the behavior of k-means on well-clustered data. In particular, we study the case when each cluster is distributed as a different Gaussian – or, in other words, when the input comes from… CONTINUE READING

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    k-Means Has Polynomial Smoothed Complexity

    VIEW 2 EXCERPTS

    Isotropic PCA and Affine-Invariant Clustering

    Learning mixtures of Gaussians

    • Sanjoy Dasgupta
    • Computer Science
    • 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039)
    • 1999