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@inproceedings{coclustering01,
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@inproceedings{yu2003multiclass,
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@article{filippone2008ska,
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@techreport{Spielman:1996,
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@techreport{Dhillon:EtAl:05,
  abstract = {Recently, a variety of clustering algorithms have been proposed to handle data that is not linearly separable. Spectral clustering and kernel k-means are two such methods that are seemingly quite different. In this paper, we show that a general weighted kernel k-means objective is mathematically equivalent to a weighted graph partitioning objective. Special cases of this graph partitioning objective include ratio cut, normalized cut and ratio association. Our equivalence has important consequences: the weighted kernel k-means algorithm may be used to directly optimize the graph partitioning objectives, and conversely, spectral methods may be used to optimize the weighted kernel k-means objective. Hence, in cases where eigenvector computation is prohibitive, we eliminate the need for any eigenvector computation for graph partitioning. Moreover, we show that the Kernighan-Lin objective can also be incorporated into our framework, leading to an incremental weighted kernel k-means algorithm for local optim ization of the objective. We further discuss the issue of convergence of weighted kernel k-means for an arbitrary graph affinity matrix and provide a number of experimental results. These results show that non-spectral methods for graph partitioning are as effective as spectral methods and can be used for problems such as image segmentation in addition to data clustering. },
  author = {Dhillon, Inderjit S. and Guan, Yuqiang and Kulis, Brian},
  institution = {University of Texas Dept. of Computer Science},
  interhash = {d4468c8f6dd6ce79177c4be9549bd545},
  intrahash = {21707c7469b7d2d19d388e729b753d91},
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}

@phdthesis{butler2008eas,
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@unpublished{butler2006-3,
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  interhash = {852cf90cdc865cd9c7985875bcde2160},
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}