@misc{Manning2009,
archivePrefix = {arXiv},
arxivId = {0521865719 9780521865715},
author = {Manning, Christopher D. and Raghavan, Prabhakar},
booktitle = {Online},
chapter = {Boolean Re},
doi = {10.1109/LPT.2009.2020494},
editor = {Salas, A Cannon-Bowers E},
eprint = {0521865719 9780521865715},
institution = {Cambridge University Press Cambridge, England},
isbn = {0521865719},
issn = {13864564},
keywords = {keyword},
pages = {1},
pmid = {10575050},
publisher = {Cambridge University Press},
title = {{An Introduction to Information Retrieval}},
url = {http://dspace.cusat.ac.in/dspace/handle/123456789/2538},
year = {2009}
}



@article{landauer_solution_1997,
	title = {A solution to {Plato}âs problem: {The} latent semantic analysis theory of acquisition, induction, and representation of knowledge},
	shorttitle = {A solution to {Plato}âs problem},
	abstract = {How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. Prologue "How much do we know at any time? Much more, or so I believe, than we know we know!" âAgatha Christie, The Moving Finger A typical American seventh grader knows the meaning of},
	journal = {Psychological review},
	author = {Landauer, Thomas K. and Dutnais, Susan T.},
	year = {1997},
	pages = {211--240},
	file = {Citeseer - Full Text PDF:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/2UIRHX2N/Landauer et Dutnais - 1997 - A solution to Platoâs problem The latent semantic.pdf:application/pdf;Citeseer - Snapshot:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/EURPMWBF/summary.html:text/html}
}

@incollection{aggarwal2012survey,
  title={A survey of text classification algorithms},
  author={Aggarwal, Charu C and Zhai, ChengXiang},
  booktitle={Mining text data},
  pages={163--222},
  year={2012},
  publisher={Springer}
}

@article{torres_similarity_2009,
	title = {A similarity measure for clustering and its applications},
	volume = {3},
	url = {https://www.cs.nmt.edu/~rbasnet/research/ClusteringSimilarityAndItsApplications.pdf},
	number = {3},
	urldate = {2016-02-23},
	journal = {Int J Electr Comput Syst Eng},
	author = {Torres, Guadalupe J. and Basnet, Ram B. and Sung, Andrew H. and Mukkamala, Srinivas and Ribeiro, Bernardete M.},
	year = {2009},
	pages = {164--170},
	file = {ClusteringSimilarityAndItsApplications.pdf:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/V54ESEW9/ClusteringSimilarityAndItsApplications.pdf:application/pdf}
}

@inproceedings{Hofmann:1999:PLS:312624.312649,
 author = {Hofmann, Thomas},
 title = {Probabilistic Latent Semantic Indexing},
 booktitle = {Proceedings of the 22Nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
 series = {SIGIR '99},
 year = {1999},
 isbn = {1-58113-096-1},
 location = {Berkeley, California, USA},
 pages = {50--57},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/312624.312649},
 doi = {10.1145/312624.312649},
 acmid = {312649},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@article{Blei:2003:LDA:944919.944937,
 author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
 title = {Latent Dirichlet Allocation},
 journal = {J. Mach. Learn. Res.},
 issue_date = {3/1/2003},
 volume = {3},
 month = mar,
 year = {2003},
 issn = {1532-4435},
 pages = {993--1022},
 numpages = {30},
 url = {http://dl.acm.org/citation.cfm?id=944919.944937},
 acmid = {944937},
 publisher = {JMLR.org},
} 


@article{griffiths_finding_2004,
	title = {Finding scientific topics},
	volume = {101},
	url = {http://www.pnas.org/content/101/suppl.1/5228.long},
	number = {Suppl 1},
	urldate = {2015-01-08},
	journal = {Proceedings of the National academy of Sciences of the United States of America},
	author = {Griffiths, Thomas L. and Steyvers, Mark},
	year = {2004},
	pages = {5228--5235},
	file = {sciencetopics.pdf:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/TUC4ZZNT/sciencetopics.pdf:application/pdf}
}

@TECHREPORT{Heinrich04parameterestimation,
    author = {Gregor Heinrich},
    title = {Parameter estimation for text analysis},
    institution = {},
    year = {2004}
}

@inproceedings{rosen-zvi_author-topic_2004,
	title = {The author-topic model for authors and documents},
	url = {http://dl.acm.org/citation.cfm?id=1036902},
	urldate = {2015-01-08},
	booktitle = {Proceedings of the 20th conference on Uncertainty in artificial intelligence},
	publisher = {{AUAI} Press},
	author = {Rosen-Zvi, Michal and Griffiths, Thomas and Steyvers, Mark and Smyth, Padhraic},
	year = {2004},
	pages = {487--494},
	file = {398.pdf:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/54RQEWDP/398.pdf:application/pdf}
}


@article{steyvers_probabilistic_2007,
	title = {Probabilistic topic models},
	volume = {427},
	url = {http://books.google.com/books?hl=en&lr=&id=JbzCzPvzpmQC&oi=fnd&pg=PA427&dq=%22matrix%3B+that+dimensionality+reduction+is+an+essential+part+of+this+derivation%3B+and+that+words%22+%22one+chooses+a+topic+at+random+according+to+this+distribution,+and+draws+a+word+from+that+topic.%22+&ots=aMH_OZO1LN&sig=7o2snQ0epSgTpXuJkwHqCkLTv6o},
	number = {7},
	urldate = {2015-01-08},
	journal = {Handbook of latent semantic analysis},
	author = {Steyvers, Mark and Griffiths, Tom},
	year = {2007},
	pages = {424--440},
	file = {SteyversGriffithsLSABookFormatted.pdf:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/2R86CFB8/SteyversGriffithsLSABookFormatted.pdf:application/pdf}
}



@book{belmandt_basics,
	title = {Basics of Pretopology},
	url = {http://ijpam.eu/contents/2013-86-1/5/5.pdf},
	urldate = {2015-01-13},
	year = {2011},
	author = {Belmandt, ZT},
	publisher = {Hermann}
}

@article{ahat_halshs00642761,
  TITLE = {{Document Classification with LSA and Pretopology.}},
  AUTHOR = {Ahat, M. and Amor S., Ben and Bui, M. and Jhean-Larose, S. and Denhiere, G.},
  URL = {https://halshs.archives-ouvertes.fr/halshs-00642761},
  JOURNAL = {{Studia Informatica Universalis}},
  PUBLISHER = {{Hermann}},
  VOLUME = {8},
  NUMBER = {1},
  YEAR = {2010},
  HAL_ID = {halshs-00642761},
  HAL_VERSION = {v1},
}

@Article{Ferligoj1992,
author="Ferligoj, A.
and Batagelj, V.",
title="Direct multicriteria clustering algorithms",
journal="Journal of Classification",
year="1992",
volume="9",
number="1",
pages="43--61",
issn="1432-1343",
doi="10.1007/BF02618467",
url="http://dx.doi.org/10.1007/BF02618467"
}

@article{Largeron2002169,
title = "A pretopological approach for structural analysis ",
journal = "Information Sciences ",
volume = "144",
number = "1–4",
pages = "169 - 185",
year = "2002",
note = "",
issn = "0020-0255",
doi = "http://dx.doi.org/10.1016/S0020-0255(02)00189-5",
url = "http://www.sciencedirect.com/science/article/pii/S0020025502001895",
author = "Christine Largeron and Stéphane Bonnevay"
}


@article{le_new,
	title = {A clustering method associating pretopological concepts and k-means algorithm},
	journal = {The 12th International Conference on Applied Stochastic Models and Data Analysis},
	year = {2007},
	author = {Thanh Van Le, Nadia Kabachi and Lamure, Michel},
	file = {ARTICLE-Le-Kabachi-Lamure-RIVF2007.pdf:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/VZWXMEC4/ARTICLE-Le-Kabachi-Lamure-RIVF2007.pdf:application/pdf}
}

@article{mdvicmb,
	title={Modeling the complex dynamics of distributed communities of the web with pretopology},
	journal={Proceedings of the 7th International Conference on Innovative Internet Community Systems},
	year={2007},
	author={Vincent Levorato and Marc Bui},

}

@book{Koller:2009:PGM:1795555,
 author = {Koller, Daphne and Friedman, Nir},
 title = {Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning},
 year = {2009},
 isbn = {0262013193, 9780262013192},
 publisher = {The MIT Press},
} 

@article{morstatter_is_2013,
	title = {Is the Sample Good Enough? Comparing Data from Twitter's Streaming {API} with Twitter's Firehose},
	shorttitle = {Is the Sample Good Enough?},
	url = {http://arxiv.org/abs/1306.5204},
	abstract = {Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming {API}" which provides a sample of all tweets matching some parameters preset by the {API} user. The {API} service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter {API} is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled {API} service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming {API}.},
	urldate = {2014-09-17},
	journal = {{arXiv}:1306.5204 [physics]},
	author = {Morstatter, Fred and Pfeffer, Jürgen and Liu, Huan and Carley, Kathleen M.},
	month = jun,
	year = {2013},
	note = {{arXiv}: 1306.5204},
	keywords = {Computer Science - Social and Information Networks, Physics - Physics and Society},
	file = {arXiv\:1306.5204 PDF:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/F2KAWWNC/Morstatter et al. - 2013 - Is the Sample Good Enough Comparing Data from Twi.pdf:application/pdf;arXiv.org Snapshot:/Users/karimsayadi/Library/Application Support/Zotero/Profiles/a484na80.default/zotero/storage/K38PMNM4/1306.html:text/html}
}