@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
}

@article{wu2008wu,
  abstract = {This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM)
in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.},
  address = {London},
  author = {Wu, Xindong and Kumar, Vipin and Quinlan, J. Ross and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and McLachlan, Geoffrey and Ng, Angus and Liu, Bing and Yu, Philip and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David and Steinberg, Dan},
  interhash = {76fd294a34cf85638f6e194a85af8db9},
  intrahash = {2c34bb4b49187a6d3e780e78d254ae1f},
  issn = {0219-1377},
  journal = {Knowledge and Information Systems},
  month = Jan,
  number = 1,
  pages = {1--37},
  publisher = {Springer},
  title = {Top 10 algorithms in data mining},
  url = {http://dx.doi.org/10.1007/s10115-007-0114-2},
  volume = 14,
  year = 2008
}

@inproceedings{1145629,
  address = {New York, NY, USA},
  author = {Desikan, Prasanna Kumar and Pathak, Nishith and Srivastava, Jaideep and Kumar, Vipin},
  booktitle = {ICWE '06: Proceedings of the 6th international conference on Web engineering},
  doi = {http://doi.acm.org/10.1145/1145581.1145629},
  interhash = {d2c5bff1a5bcbcb1dcf2e3fdfb81a874},
  intrahash = {32b98aca2e38ee638d3aea77dddea2a2},
  isbn = {1-59593-352-2},
  location = {Palo Alto, California, USA},
  month = {July},
  pages = {233--240},
  publisher = {ACM Press},
  title = {Divide and conquer approach for efficient pagerank computation},
  url = {http://portal.acm.org/citation.cfm?doid=1145581.1145629},
  year = 2006
}

@misc{ieKey,
  author = {Boley, Daniel and Gini, Maria and Gross, Robert and Han, Eui-Hong (Sam) and Hastings, Kyle and Karypis, George and Kumar, Vipin and Mobasher, Bamshad and Moore, Jerome},
  date = {1999},
  interhash = {d544ef5463da700ac7209b61b5bc7eef},
  intrahash = {1a1d7962e0dbc3b0afac99911db093e1},
  journal = {To appear in Decision Support Systems Journal},
  title = {"Partitioning-Based Clustering for Web Document Categorization},
  year = 1999
}

@article{han98hypergraph,
  author = {Han, Eui-Hong and Karypis, George and Kumar, Vipin and Mobasher, Bamshad},
  interhash = {3bb7fb3fd3af41fac2db5460a5acfd2c},
  intrahash = {9723b092d975dedb8f6d5f711bb00ffd},
  journal = {Data Engineering Bulletin},
  number = 1,
  pages = {15-22},
  title = {Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results},
  url = {http://citeseer.ist.psu.edu/han98hypergraph.html},
  volume = 21,
  year = 1998
}

@inproceedings{ertoez02,
  author = {Ertoz, Levent and Steinbach, Michael and Kumar, Vipin},
  booktitle = {Workshop on Clustering High Dimensional Data and its Applications at 2nd SIAM International Conference on Data Mining},
  interhash = {006217972810ce35c05fc6019de1cd6e},
  intrahash = {ba0e3067111d2e3eea9a4d9fc995e36b},
  title = {A New Shared Nearest Neighbor Clustering Algorithm and its Applications},
  year = 2002
}

@inproceedings{Steinbach03,
  author = {Steinbach, Michael and Ertoz, Levent and Kumar, Vipin},
  booktitle = {New Vistas in Statistical Physics -- Applications in Econophysics, Bioinformatics, and Pattern Recognition},
  editor = {Wille, L. T.},
  interhash = {e4382b8b1893c6a662c075146aaaafb9},
  intrahash = {556f3a4867ec99b175cc74d1ac9c60f7},
  publisher = {Springer-Verlag},
  title = {Challenges of Clustering High Dimensional Data},
  year = 2003
}