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  title = {{Partitioning Sparse Matrices with Eigenvectors of Graphs}},
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  number = 9,
  pages = {1088-1096},
  title = {Spectral K-way ratio-cut partitioning and clustering.},
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@inproceedings{yu2003multiclass,
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  author = {Yu, Stella X. and Shi, Jianbo},
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  intrahash = {d73e1ffedf586cdbaf076277e7c1add6},
  month = oct,
  title = {Multiclass Spectral Clustering},
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@article{journals/tcad/HagenK92,
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@inproceedings{Karypis98multilevelk-way,
  abstract = {In this paper, we present a new multilevel k-way hypergraph partitioning algorithm that substantially outperforms the existing state-of-the-art K-PM/LR algorithm for multi-way partitioning. both for optimizing local as well as global objectives. Experiments on the ISPD98 benchmark suite show that the partitionings produced by our scheme are on the average 15% to 23% better than those produced by the K-PM/LR algorithm, both in terms of the hyperedge cut as well as the (K  -  1) metric. Furthermore, our algorithm is significantly faster, requiring 4 to 5 times less time than that required by K-PM/LR. 1 Introduction  Hypergraph partitioning is an important problem with extensive application to many areas, including VLSI design [10], efficient storage of large databases on disks [14], and data mining [13]. The problem is to partition the vertices of a hypergraph into k roughly equal parts, such that a certain objective function defined over the hyperedges is optimized. A commonly used obje...},
  author = {Karypis, George and Kumar, Vipin},
  booktitle = {In Proceedings of the Design and Automation Conference},
  interhash = {413d89f472133bf5ff0671cccc818f55},
  intrahash = {d63a73732f65ce10595e210cedda3bd1},
  pages = {343--348},
  title = {Multilevel k-way Hypergraph Partitioning},
  year = 1998
}