@electronic{www2007.org,
   title       = {WWW2007 Paper Details}, 
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@electronic{www.rashmisinha.com,
   title       = {Clustering comes to Flickr}, 
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@electronic{cran.r-project.org,
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@electronic{www.cs.utexas.edu,
   title       = {Graclus: a new efficient graph clustering software}, 
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@electronic{www.cs.utexas.edu,
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@electronic{fuzzy.cs.uni-magdeburg.de,
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@electronic{www.ll.mit.edu,
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@electronic{portal.acm.org,
   title       = {Document clustering with committees}, 
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@electronic{mipav.cit.nih.gov,
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@electronic{ihome.cuhk.edu.hk,
   title       = {Microarray software (R packages for microarray analysis)}, 
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@electronic{www.cs.waikato.ac.nz,
   title       = {Weka 3 - Data Mining with Open Source Machine Learning Software in Java}, 
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@electronic{glaros.dtc.umn.edu,
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@electronic{tangra.si.umich.edu,
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@electronic{pil.phys.uniroma1.it,
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@electronic{www.kde.cs.uni-kassel.de,
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@electronic{crd.lbl.gov,
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@electronic{www.cs.albany.edu,
   title       = {Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results}, 
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   description = {Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results 

Ian Davidson1  and S.S. Ravi1 
(1) 	Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222,  },
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@electronic{www.lans.ece.utexas.edu,
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@electronic{www.stat.washington.edu,
   title       = {Adrian E. Raftery}, 
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@electronic{vlado.fmf.uni-lj.si,
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@electronic{cscs.umich.edu,
   title       = {Community Discovery Methods for Complex Networks}, 
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@electronic{www.carrot2.org,
   title       = {Carrot2 project}, 
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@electronic{www.stat.washington.edu,
   title       = {Adrian Raftery: Unpublished Technical Reports}, 
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   description = {Handcock, M.S., Raftery, A.E. and Tantrum, J. (2005).

Model-Based Clustering for Social Networks.
Working Paper no. 46, Center for Statistics and the Social Sciences,
University of Washington.},
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@electronic{kt.ijs.si,
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@electronic{www.cs.cas.cz,
   title       = {Cluster list of papers}, 
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@electronic{www.isi.edu,
   title       = {CBC}, 
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@electronic{www.informatics.bangor.ac.uk,
   title       = {dataset}, 
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@electronic{www.mathematik.uni-marburg.de,
   title       = {Fundamental Clustering Problem Suite | Databionics}, 
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@electronic{www.di.ubi.pt,
   title       = {| Gaël Harry Dias - Department of Computer Science - UBI}, 
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   description = {- Dias, G. & Alves, E. (2005). Language-Independent Informative Topic Segmentation. In Proceedings of the 9th International Symposium on Social Communication. Santiago de Cuba, Cuba, January 24-28. pp. 588-592. ISBN: 9597174057. [pdf]},
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@electronic{www.cs.albany.edu,
   title       = {Ian Davidson, Mining, Machine Learning, MML, Data Mining}, 
   url         = {http://www.cs.albany.edu/~davidson/},
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@electronic{websom.hut.fi,
   title       = {WEBSOM - A novel SOM-based approach to free-text mining}, 
   url         = {http://websom.hut.fi/websom/},
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@electronic{www.citeulike.org,
   title       = {CiteULike: Tag clustering}, 
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@electronic{snaket.di.unipi.it,
   title       = {Search SnakeT Clustering Engine, Meta Search Cluster MetaSearch}, 
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@electronic{clusty.com,
   title       = {Clusty the Clustering Engine}, 
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@electronic{vivisimo.com,
   title       = {Vivísimo // Vivisimo Clustering - automatic categorization and meta-search software}, 
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@electronic{www.cs.rutgers.edu,
   title       = {Data Clustering: A Review}, 
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@electronic{www.csee.umbc.edu,
   title       = {Tutorial on Document Clustering}, 
   url         = {http://www.csee.umbc.edu/~nicholas/clustering/},
   biburl      = {https://puma.uni-kassel.de/url/e8417ba93ffa0fd098e60d256fdb568b/hotho},
   keywords    = {clustering tutorial text survey},
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@electronic{www.i-cherubini.it,
   title       = {Spatial Clustering Methods in Data Mining: A Survey at Mauro Cherubini’s moleskine}, 
   url         = {http://www.i-cherubini.it/mauro/blog/2005/09/21/spatial-clustering-methods-in-data-mining-a-survey/},
   biburl      = {https://puma.uni-kassel.de/url/6acb2caae6a3bf094c42be9ad4bc73af/hotho},
   keywords    = {clustering to:bibtex survey},
   added-at    = {2006-01-17T11:34:59.000+0100},
   description = {J. Han, M. Kamber, and A. K. H. Tung. Geographic Data Mining and Knowledge Discovery, chapter Spatial Clustering Methods in Data Mining: A Survey, pages 1–29. Taylor and Francis, 2001. [url]},
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@electronic{crd.lbl.gov,
   title       = {Chris Ding Publications}, 
   url         = {http://crd.lbl.gov/~cding/papers/spectral.html},
   biburl      = {https://puma.uni-kassel.de/url/844c0f7b3dc41059c2136eed49d7aba0/hotho},
   keywords    = {clustering},
   added-at    = {2005-10-21T14:56:49.000+0200},
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