@incollection{ganter2010basic,
  abstract = {We describe two algorithms for closure systems. The purpose of the first is to produce all closed sets of a given closure operator. The second constructs a minimal family of implications for the ”logic” of a closure system. These algorithms then are applied to problems in concept analysis: Determining all concepts of a given context and describing the dependencies between attributes. The problem of finding all concepts is equivalent, e.g., to finding all maximal complete bipartite subgraphs of a bipartite graph.},
  author = {Ganter, Bernhard},
  booktitle = {Formal Concept Analysis},
  doi = {10.1007/978-3-642-11928-6_22},
  editor = {Kwuida, Léonard and Sertkaya, Barış},
  interhash = {f44d214d7176b9183d2bf29b8efbdc00},
  intrahash = {00d3cdaea05efaed9cb99c94f93ddacc},
  isbn = {978-3-642-11927-9},
  language = {English},
  pages = {312-340},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Two Basic Algorithms in Concept Analysis},
  url = {http://dx.doi.org/10.1007/978-3-642-11928-6_22},
  volume = 5986,
  year = 2010
}

@misc{Xu09relevanceranking,
  author = {Xu, Jun and Li, Hang and Zhong, Chaoliang},
  interhash = {c0a86e1785768ef1f15d5cacc1442597},
  intrahash = {4d086714a580d80c68077fcc98656db3},
  title = {Relevance Ranking using Kernels},
  url = {http://www.google.de/url?sa=t&source=web&cd=2&ved=0CCEQFjAB&url=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F81437%2FMSR_TechReport_2009_Kernel4IR.pdf&rct=j&q=Relevance%20Ranking%20using%20Kernels&ei=uzftTM28GMr2sgaO4Y35Dg&usg=AFQjCNFftCUJMs7LgoqEXR2VvT7bQ7FWHw&sig2=H5OBpauNrYXJ0asAFrEuGQ&cad=rja},
  year = 2009
}

@misc{Maslov2009,
  abstract = {  We review our recent work on applying the Google PageRank algorithm to find scientific "gems" among all Physical Review publications, and its extension to CiteRank, to find currently popular research directions. These metrics provide a meaningful extension to traditionally-used importance measures, such as the number of citations and journal impact factor. We also point out some pitfalls of over-relying on quantitative metrics to evaluate scientific quality. },
  author = {Maslov, Sergei and Redner, S.},
  interhash = {8f0a3a222a5c357e4db423ec065065da},
  intrahash = {d2b34ecaa23078ebef7a7ee84be509a4},
  note = {cite arxiv:0901.2640 Comment: 3 pages, 1 figure, invited comment for the Journal of Neuroscience.   The arxiv version is microscopically different from the published version},
  title = {Promise and Pitfalls of Extending Google's PageRank Algorithm to   Citation Networks},
  url = {http://arxiv.org/abs/0901.2640},
  year = 2009
}

@article{journals/tkde/BrunoW07,
  author = {Bruno, Nicolas and Wang, Hui},
  date = {2007-06-05},
  ee = {http://dx.doi.org/10.1109/TKDE.2007.1011},
  interhash = {10808bc663c45993fb1f7fb6cf8cadd9},
  intrahash = {e858b8ea4131ca83354910e12d26b389},
  journal = {IEEE Trans. Knowl. Data Eng.},
  number = 4,
  pages = {523-537},
  title = {The Threshold Algorithm: From Middleware Systems to the Relational Engine.},
  url = {http://dblp.uni-trier.de/db/journals/tkde/tkde19.html#BrunoW07},
  volume = 19,
  year = 2007
}

@inproceedings{citeulike:2801543,
  abstract = {: Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, or combining rule, such as min or average, that combines the individual grades to obtain an...},
  author = {Fagin, Ronald and Lotem, Amnon and Naor, Moni},
  booktitle = {Symposium on Principles of Database Systems},
  citeulike-article-id = {2801543},
  interhash = {8bbc6d283a09e8ec8c082496b2f25865},
  intrahash = {5fae1d60624767e4b3dea4d1a985cf7c},
  posted-at = {2008-05-15 13:57:01},
  priority = {0},
  title = {Optimal Aggregation Algorithms for Middleware},
  url = {http://citeseer.ist.psu.edu/441654.html},
  year = 2001
}

@inproceedings{conf/sigmod/WangWYY02,
  author = {Wang, Haixun and 0010, Wei Wang and Yang, Jiong and Yu, Philip S.},
  booktitle = {SIGMOD Conference},
  crossref = {conf/sigmod/2002},
  date = {2009-06-28},
  editor = {Franklin, Michael J. and Moon, Bongki and Ailamaki, Anastassia},
  ee = {http://doi.acm.org/10.1145/564691.564737},
  interhash = {9da0e61a2ac3ac371edfb251fbbfc2ae},
  intrahash = {5ad941d8f0a06bb5e570e22a8cc58d92},
  isbn = {1-58113-497-5},
  pages = {394-405},
  publisher = {ACM},
  title = {Clustering by pattern similarity in large data sets.},
  url = {http://dblp.uni-trier.de/db/conf/sigmod/sigmod2002.html#WangWYY02},
  year = 2002
}

@inproceedings{1283494,
  address = {Philadelphia, PA, USA},
  author = {Arthur, David and Vassilvitskii, Sergei},
  booktitle = {SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
  interhash = {0be633834158a3c9cba959406c3e1964},
  intrahash = {553bbfa74b13c47b4e9c7c0034a8406e},
  isbn = {978-0-898716-24-5},
  location = {New Orleans, Louisiana},
  pages = {1027--1035},
  publisher = {Society for Industrial and Applied Mathematics},
  title = {k-means++: the advantages of careful seeding},
  year = 2007
}