@unpublished{ranade:sus,
  author = {Ranade, A.G.},
  interhash = {473e92f688426631dd9ccc7639a5e861},
  intrahash = {9f7f1562631792a85f2dd5f5eefbcf4d},
  title = {{Some uses of spectral methods}},
  year = 2000
}

@article{Boley97principaldirection,
  abstract = {We propose a new algorithm capable of partitioning a set of documents or other samples based on an embedding in a high dimensional Euclidean space (i.e. in which every document is a vector of real numbers). The method is unusual in that it is divisive, as opposed to agglomerative, and operates by repeatedly splitting clusters into smaller clusters. The splits are not based on any distance or similarity measure. The documents are assembled in to a matrix which is very sparse. It is this sparsity that permits the algorithm to be very efficient. The performance of the method is illustrated with a set of text documents obtained from the World Wide Web. Some possible extensions are proposed for further investigation.},
  author = {Boley, Daniel},
  interhash = {281afd06bd3e21ec3ef212da4ec18ee0},
  intrahash = {bca740460f14035af773f665887b6fa4},
  journal = {Data Mining and Knowledge Discovery},
  pages = {325--344},
  title = {Principal Direction Divisive Partitioning},
  volume = 2,
  year = 1997
}

@inproceedings{OsinskiSW04,
  author = {Osinski, Stanislaw and Stefanowski, Jerzy and Weiss, Dawid},
  booktitle = {Intelligent Information Systems},
  crossref = {ConfIis2004},
  interhash = {ee4c7c8946a283da5d65103ce8f77a81},
  intrahash = {40aba631c1ac8819bd64b0ee74bfdd1b},
  pages = {359-368},
  title = {Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition},
  year = 2004
}

@inproceedings{Approximating2008Java,
  abstract = {In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the"long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general. },
  author = {Java, Akshay and Joshi, Anupam and FininBook, Tim},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  date = {2008 Abstract:},
  interhash = {ede357e110fee8803dc181d262f30087},
  intrahash = {386f36679c111f30e37ced272d5b355c},
  publisher = {AAAI Press},
  title = {Approximating the Community Structure of the Long Tail},
  url = {http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail},
  year = 2008
}

@techreport{berry94using,
  author = {Berry, Michael W. and Dumais, Susan T. and O'Brien, Gavin W.},
  institution = {Computer Science Department, University of Tennessee, Knoxville},
  interhash = {e549ee9043c39e08ec025d5b4b2111f5},
  intrahash = {bcaa7d0ba815e0f2796616c5504fbeff},
  number = {UT-CS-94-270},
  title = {Using Linear Algebra for Intelligent Information Retrieval},
  url = {http://citeseer.ist.psu.edu/berry95using.html},
  year = 1994
}