@incollection{springerlink:10.1007/978-3-540-74839-7_12,
  abstract = {Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts, and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomaration approach.},
  address = {Berlin / Heidelberg},
  affiliation = {Department of Computer and Information Science, University of Konstanz},
  author = {Brandes, Ulrik and Delling, Daniel and Gaertler, Marco and Görke, Robert and Hoefer, Martin and Nikoloski, Zoran and Wagner, Dorothea},
  booktitle = {Graph-Theoretic Concepts in Computer Science},
  doi = {10.1007/978-3-540-74839-7_12},
  editor = {Brandstädt, Andreas and Kratsch, Dieter and Müller, Haiko},
  interhash = {b335302041d1865d7cfec7467e8e2999},
  intrahash = {6fd10991ee4e3880c64c11862884ead7},
  isbn = {978-3-540-74838-0},
  keyword = {Computer Science},
  openurl = {http://www.blub.de},
  pages = {121-132},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {On Finding Graph Clusterings with Maximum Modularity},
  url = {http://dx.doi.org/10.1007/978-3-540-74839-7_12},
  volume = 4769,
  year = 2007
}

@article{newman2006modularity,
  abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.},
  author = {Newman, M. E. J.},
  doi = {10.1073/pnas.0601602103},
  interhash = {e664336d414a1e21d89f30cc56f5e739},
  intrahash = {5dd9d0c2155f242393e63547d8a2347f},
  journal = {Proceedings of the National Academy of Sciences},
  number = 23,
  pages = {8577--8582},
  title = {Modularity and community structure in networks},
  volume = 103,
  year = 2006
}

@electronic{nedjah2009intelligent,
  abstract = {"Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exists (intelligent web search, data mining, law enforcement, etc.). Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing." "This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering."--Jacket.},
  address = {Berlin},
  author = {Nedjah, Nadia},
  interhash = {fe4dc424274eac3c1588fda8bfa5290a},
  intrahash = {1a61a34d4984ee4451be75902c25c49b},
  isbn = {9783540856443 3540856447 9783540856436 3540856439},
  publisher = {Springer},
  refid = {656393969},
  title = {Intelligent text categorization and clustering},
  url = {http://rave.ohiolink.edu/ebooks/ebc/9783540856443},
  year = 2009
}

@article{Jain:1999:DCR:331499.331504,
  acmid = {331504},
  address = {New York, NY, USA},
  author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
  doi = {10.1145/331499.331504},
  interhash = {5113b61d428d4de4423182e5f2b2f468},
  intrahash = {b19bcef82a04eb82ee4abde53ee7d1c2},
  issn = {0360-0300},
  issue_date = {Sept. 1999},
  journal = {ACM Comput. Surv.},
  month = sep,
  number = 3,
  numpages = {60},
  pages = {264--323},
  publisher = {ACM},
  title = {Data clustering: a review},
  url = {http://doi.acm.org/10.1145/331499.331504},
  volume = 31,
  year = 1999
}

@article{RePEc:eee:csdana:v:41:y:2002:i:1:p:59-90,
  abstract = {No abstract is available for this item.},
  author = {Dhillon, Inderjit S. and Modha, Dharmendra S. and Spangler, W. Scott},
  interhash = {3ff82dddf6ce4d86909347824554ddf8},
  intrahash = {03e92f40796a0093a6e882a83f5cd995},
  journal = {Computational Statistics \& Data Analysis},
  month = {November},
  number = 1,
  pages = {59-90},
  title = {Class visualization of high-dimensional data with applications},
  url = {http://www.cs.utexas.edu/~inderjit/public_papers/csda.pdf},
  volume = 41,
  year = 2002
}