k-means-: A Unified Approach to Clustering and Outlier Detection

@inproceedings{Chawla2013kmeansAU,
  title={k-means-: A Unified Approach to Clustering and Outlier Detection},
  author={Sanjay Chawla and Aristides Gionis},
  booktitle={SDM},
  year={2013}
}
We present a unified approach for simultaneously clustering and discovering outliers in data. Our approach is formalized as a generalization of the k-means problem. We prove that the problem is NP-hard and then present a practical polynomial time algorithm, which is guaranteed to converge to a local optimum. Furthermore we extend our approach to all distance measures that can be expressed in the form of a Bregman divergence. Experiments on synthetic and real datasets demonstrate the… CONTINUE READING
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  • In particular on the famous KDD cup network-intrusion dataset, we were able to increase the precision of the outlier detection task by nearly 100% compared to the classical nearest-neighbor approach.

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K-means Clustering with Outlier Removal

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