@article{haveliwala8090seg,
  author = {Haveliwala, T.H. and Kamvar, S.D.},
  interhash = {0d40b575bd05c58b401f7a78ad3d4627},
  intrahash = {bc3bddcd6ea80eea5716492751bdff36},
  journal = {A Stanford University Technical Report http://dbpubs. stanford. edu},
  title = {{The second eigenvalue of the Google matrix}},
  year = 2003
}

@techreport{Spielman:1996,
  address = {Berkeley, CA, USA},
  author = {Spielman, Daniel A. and Teng, Shang},
  interhash = {83f3d15605beda920551830ccac3d79a},
  intrahash = {06b1b19e0a29a145555cb1526716c451},
  publisher = {University of California at Berkeley},
  title = {Spectral Partitioning Works: Planar Graphs and Finite Element Meshes},
  year = 1996
}

@article{mohar1997sal,
  author = {Mohar, B.},
  interhash = {04fbb3036b34072e6d27e7a84cfafb87},
  intrahash = {df49696a4bc6df9a662c95fe8f713beb},
  journal = {Graph Symmetry: Algebraic Methods and Applications},
  pages = {227--275},
  publisher = {Kluwer},
  title = {{Some applications of Laplace eigenvalues of graphs}},
  volume = 497,
  year = 1997
}

@inproceedings{coclustering01,
  address = {New York, NY, USA},
  author = {Dhillon, Inderjit S.},
  booktitle = {KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining},
  citeulike-article-id = {1168281},
  doi = {10.1145/502512.502550},
  interhash = {d112b6041dab4b4677368c7bf101f1ee},
  intrahash = {f07d9cc4813f3ecda75e6f0c8025cece},
  isbn = {158113391X},
  pages = {269--274},
  priority = {2},
  publisher = {ACM Press},
  title = {Co-clustering documents and words using bipartite spectral graph partitioning},
  url = {http://portal.acm.org/citation.cfm?id=502512.502550},
  year = 2001
}

@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},
  number = {TR-04-25},
  title = {A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts},
  url = {http://www.cs.utexas.edu/ftp/pub/techreports/tr04-25.pdf},
  year = 2005
}