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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Morstatter, F., ürgen Pfeffer, J., Liu, H. & Carley, K.M. Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose 2013   article URL 
    BibTeX:
    @article{morstatter2013sample,
      author = {Morstatter, Fred and ürgen Pfeffer, J and Liu, Huan and Carley, Kathleen M},
      title = {Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose},
      year = {2013},
      url = {http://scholar.google.de/scholar.bib?q=info:NkS2afIrqyQJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0}
    }
    
    Bullock, B.N., Jäschke, R. & Hotho, A. Tagging data as implicit feedback for learning-to-rank 2011 Proceedings of the ACM WebSci'11  inproceedings URL 
    BibTeX:
    @inproceedings{bullock2011tagging,
      author = {Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas},
      title = {Tagging data as implicit feedback for learning-to-rank},
      booktitle = {Proceedings of the ACM WebSci'11},
      year = {2011},
      url = {http://journal.webscience.org/463/}
    }
    
    Hotho, A., Ulslev Pedersen, R. & Wurst, M. Ubiquitous Data 2010 Lecture Notes in Computer Science(6202), pp. 61-74  article URL 
    BibTeX:
    @article{hotho2010ubiquitous,
      author = {Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael},
      title = {Ubiquitous Data},
      journal = {Lecture Notes in Computer Science},
      publisher = {Springer},
      year = {2010},
      number = {6202},
      pages = {61--74},
      url = {http://rd.springer.com/content/pdf/10.1007%2F978-3-642-16392-0_4.pdf}
    }
    
    Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of Predictability in Human Mobility 2010 Science
    Vol. 327(5968), pp. 1018-1021 
    article DOI URL 
    Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual’s trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
    BibTeX:
    @article{Song19022010,
      author = {Song, Chaoming and Qu, Zehui and Blumm, Nicholas and Barabási, Albert-László},
      title = {Limits of Predictability in Human Mobility},
      journal = {Science},
      year = {2010},
      volume = {327},
      number = {5968},
      pages = {1018-1021},
      url = {http://www.sciencemag.org/content/327/5968/1018.abstract},
      doi = {http://dx.doi.org/10.1126/science.1177170}
    }
    
    Atzmueller, M., Lemmerich, F., Krause, B. & Hotho, A. Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description 2009 Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases  inproceedings URL 
    BibTeX:
    @inproceedings{ALKH:09,
      author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas},
      title = {Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description},
      booktitle = {Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
      year = {2009},
      note = {accepted},
      url = {http://www.ke.tu-darmstadt.de/events/LeGo-09/04-Atzmueller.pdf}
    }
    
    Atzmueller, M., Lemmerich, F., Krause, B. & Hotho, A. Who are the Spammers? Understandable Local Patterns for Concept Description 2009 7th Conference on Computer Methods and Systems  inproceedings URL 
    BibTeX:
    @inproceedings{atze09,
      author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas},
      title = {Who are the Spammers? Understandable Local Patterns for Concept Description},
      booktitle = {7th Conference on Computer Methods and Systems},
      year = {2009},
      note = {ISBN 83-916420-5-4},
      url = {http://www.cms.agh.edu.pl/}
    }
    
    Krause, B., Schmitz, C., Hotho, A. & Stumme, G. The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems 2008 AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, pp. 61-68  inproceedings DOI URL 
    BibTeX:
    @inproceedings{anti2008krause,
      author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd},
      title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems},
      booktitle = {AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web},
      publisher = {ACM},
      year = {2008},
      pages = {61--68},
      url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf},
      doi = {http://doi.acm.org/10.1145/1451983.1451998}
    }
    
    From Web to Social Web: Discovering and Deploying User and Content Profiles 2007
    Vol. 4736 
    book URL 
    Abstract: This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.
    BibTeX:
    @book{Berendt2007,,
      title = {From Web to Social Web: Discovering and Deploying User and Content Profiles },
      publisher = {Springer},
      year = {2007},
      volume = {4736},
      url = {http://www.springer.com/dal/home?SGWID=1-102-22-173759307-0&changeHeader=true&referer=www.springeronline.com&SHORTCUT=www.springer.com/978-3-540-74950-9}
    }
    
    Wurst, M. & Morik, K. Distributed feature extraction in a p2p setting: a case study 2007 Future Gener. Comput. Syst.
    Vol. 23(1), pp. 69-75 
    article DOI URL 
    BibTeX:
    @article{1276056,
      author = {Wurst, Michael and Morik, Katharina},
      title = {Distributed feature extraction in a p2p setting: a case study},
      journal = {Future Gener. Comput. Syst.},
      publisher = {Elsevier Science Publishers B. V.},
      year = {2007},
      volume = {23},
      number = {1},
      pages = {69--75},
      url = {http://portal.acm.org/citation.cfm?id=1276056},
      doi = {http://dx.doi.org/10.1016/j.future.2006.04.004}
    }
    
    Baeza-Yates, R., Calderón-Benavides, L. & González-Caro, C. The Intention Behind Web Queries 2006 String Processing and Information Retrieval, pp. 98-109  article URL 
    Abstract: The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful
    offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automaticway, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of auser and the categories in which these aims are situated. A manual classification of the queries was made in order to havea reference point and then we applied supervised and unsupervised learning techniques. The results obtained show that fora considerable amount of cases supervised learning is a good option, however through unsupervised learning we found relationshipsbetween users and behaviors that are not easy to detect just taking the query words. Also, through unsupervised learning weestablished that there are categories that we are not able to determine in contrast with other classes that were not consideredbut naturally appear after the clustering process. This allowed us to establish that the combination of supervised and unsupervisedlearning is a good alternative to find user’s goals. From supervised learning we can identify the user interest given certainestablished goals and categories; on the other hand, with unsupervised learning we can validate the goals and categories used,refine them and select the most appropriate to the user’s needs.
    BibTeX:
    @article{keyhere,
      author = {Baeza-Yates, Ricardo and Calderón-Benavides, Liliana and González-Caro, Cristina},
      title = {The Intention Behind Web Queries},
      journal = {String Processing and Information Retrieval},
      year = {2006},
      pages = {98--109},
      url = {http://dx.doi.org/10.1007/11880561_9}
    }
    
    Balakrishnan, H. & Deo, N. Discovering communities in complex networks. 2006 ACM Southeast Regional Conference, pp. 280-285  inproceedings URL 
    BibTeX:
    @inproceedings{conf/ACMse/BalakrishnanD06,
      author = {Balakrishnan, Hemant and Deo, Narsingh},
      title = {Discovering communities in complex networks.},
      booktitle = {ACM Southeast Regional Conference},
      publisher = {ACM},
      year = {2006},
      pages = {280-285},
      url = {http://www.cs.ucf.edu/csdept/faculty/deo/ACMSE-06.pdf}
    }
    
    Ontology Learning from Text: Methods, Evaluation and Applications 2005
    Vol. 123 
    book  
    BibTeX:
    @book{buitelaar05ontologylearningbook,,
      title = {Ontology Learning from Text: Methods, Evaluation and Applications},
      publisher = {IOS Press},
      year = {2005},
      volume = {123}
    }
    
    Weiss, S.M., Indurkhya, N. & Zhang, T. Text Mining. Predictive Methods for Analyzing Unstructured Information 2004   book URL 
    BibTeX:
    @book{0387954333,
      author = {Weiss, Sholom M. and Indurkhya, Nitin and Zhang, T.},
      title = {Text Mining. Predictive Methods for Analyzing Unstructured Information},
      publisher = {Springer, Berlin},
      year = {2004},
      edition = {1},
      url = {http://www.amazon.de/gp/redirect.html%3FASIN=0387954333%26tag=ws%26lcode=xm2%26cID=2025%26ccmID=165953%26location=/o/ASIN/0387954333%253FSubscriptionId=13CT5CVB80YFWJEPWS02}
    }
    
    Baldi, P., Frasconi, P. & Smyth, P. Modeling the Internet and the Web: Probabilistic Methods and Algorithms 2003 Modeling the Internet and the Web: Probabilistic Methods and Algorithms  inbook URL 
    Abstract: Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.  Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.  Takes a modern approach based on mathematical, probabilistic, and graphical modeling.  Provides an integrated presentation of theory, examples, exercises and applications.  Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.
    BibTeX:
    @inbook{baldi03modelling,
      author = {Baldi, Pierre and Frasconi, Paolo and Smyth, Padhraic},
      title = {Modeling the Internet and the Web: Probabilistic Methods and Algorithms},
      booktitle = {Modeling the Internet and the Web: Probabilistic Methods and Algorithms},
      publisher = {Wiley},
      year = {2003},
      url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470849061.html}
    }
    
    Dhillon, I.S., Modha, D.S. & Spangler, W.S. Class visualization of high-dimensional data with applications 2002 Computational Statistics & Data Analysis
    Vol. 41(1), pp. 59-90 
    article URL 
    Abstract: No abstract is available for this item.
    BibTeX:
    @article{RePEc:eee:csdana:v:41:y:2002:i:1:p:59-90,
      author = {Dhillon, Inderjit S. and Modha, Dharmendra S. and Spangler, W. Scott},
      title = {Class visualization of high-dimensional data with applications},
      journal = {Computational Statistics & Data Analysis},
      year = {2002},
      volume = {41},
      number = {1},
      pages = {59-90},
      url = {http://www.cs.utexas.edu/~inderjit/public_papers/csda.pdf}
    }
    
    Thrun, S., Burgard, W. & Fox, D. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) 2001   book URL 
    BibTeX:
    @book{thrun2001,
      author = {Thrun, Sebastian and Burgard, Wolfram and Fox, Dieter},
      title = {Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)},
      year = {2001},
      url = {http://www.amazon.com/Probabilistic-Robotics-Intelligent-Autonomous-Agents/dp/0262201623/ref=sr_11_1/105-3361811-4085215?ie=UTF8&qid=1190743235&sr=11-1}
    }
    
    Web Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers 2000
    Vol. 1836WEBKDD 
    proceedings  
    BibTeX:
    @proceedings{DBLP:conf/kdd/1999web,,
      title = {Web Usage Analysis and User Profiling, International WEBKDD'99
    
    Workshop, San Diego, California, USA, August 15, 1999, Revised
    Papers}, booktitle = {WEBKDD}, publisher = {Springer}, year = {2000}, volume = {1836} }
    Pyle, D. Data Preparation for Data Mining 1999   book  
    BibTeX:
    @book{books/mk/Pyle99,
      author = {Pyle, Dorian},
      title = {Data Preparation for Data Mining},
      publisher = {Morgan Kaufmann},
      year = {1999}
    }
    
    Fayyad, U.M., Piatetsky-Shapiro, G. & Smyth, P. From Data Mining to Knowledge Discovery: An Overview. 1996 Advances in Knowledge Discovery and Data Mining, pp. 1-34  incollection URL 
    BibTeX:
    @incollection{books/mit/fayyadPSU96/FayyadPS96,
      author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
      title = {From Data Mining to Knowledge Discovery: An Overview.},
      booktitle = {Advances in Knowledge Discovery and Data Mining},
      year = {1996},
      pages = {1-34},
      url = {http://dblp.uni-trier.de/db/books/collections/fayyad96.html#FayyadPS96}
    }
    
    Flajolet, P. & Martin, G.N. Probabilistic Counting Algorithms for Data Base Applications 1985 Journal of Computer and System Sciences
    Vol. 31(2), pp. 182-209 
    article URL 
    BibTeX:
    @article{flajolet85probabilistic,
      author = {Flajolet, Philippe and Martin, G. Nigel},
      title = {Probabilistic Counting Algorithms for Data Base Applications},
      journal = {Journal of Computer and System Sciences},
      year = {1985},
      volume = {31},
      number = {2},
      pages = {182-209},
      url = {http://citeseer.ist.psu.edu/flajolet85probabilistic.html}
    }
    

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