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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Recommender Systems Handbook 2011 Recommender Systems Handbook  book URL 
    BibTeX:
    @book{reference/rsh/2011,,
      title = {Recommender Systems Handbook},
      booktitle = {Recommender Systems Handbook},
      publisher = {Springer},
      year = {2011},
      url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html}
    }
    
    Herrera, F., Carmona, C., González, P. & del Jesus, M. An overview on subgroup discovery: foundations and applications 2010 Knowledge and Information Systems, pp. 1-31  article DOI URL 
    Abstract: Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.
    BibTeX:
    @article{springerlink:10.1007/s10115-010-0356-2,
      author = {Herrera, Franciso and Carmona, Cristóbal and González, Pedro and del Jesus, María},
      title = {An overview on subgroup discovery: foundations and applications},
      journal = {Knowledge and Information Systems},
      publisher = {Springer London},
      year = {2010},
      pages = {1-31},
      url = {http://dx.doi.org/10.1007/s10115-010-0356-2},
      doi = {http://dx.doi.org/10.1007/s10115-010-0356-2}
    }
    
    Hotho, A., Nürnberger, A. & Paaß, G. A Brief Survey of Text Mining 2005 LDV Forum - GLDV Journal for Computational Linguistics and Language Technology
    Vol. 20(1), pp. 19-62 
    article URL 
    BibTeX:
    @article{hotho-etal-ldv-2005,
      author = {Hotho, Andreas and Nürnberger, Andreas and Paaß, Gerhard},
      title = { A Brief Survey of Text Mining},
      journal = {LDV Forum - GLDV Journal for Computational Linguistics and Language Technology},
      year = {2005},
      volume = {20},
      number = {1},
      pages = {19-62},
      url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf}
    }
    
    Handbook on Ontologies 2003   book  
    BibTeX:
    @book{staab04handbook,,
      title = {Handbook on Ontologies},
      publisher = {Springer},
      year = {2003}
    }
    
    Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential 2002   book  
    BibTeX:
    @book{Fensel:2002,,
      title = {Spinning the Semantic Web: Bringing the World Wide Web
    
    to Its Full Potential}, publisher = {MIT Press}, year = {2002} }
    Höppner, F., Klawonn, F., Kruse, R. & Runkler, T. Fuzzy Cluster Analysis 1999   book  
    BibTeX:
    @book{hoeppner1999fuzzy,
      author = {Höppner, Frank and Klawonn, Frank and Kruse, Rudolf and Runkler, Thomas},
      title = {Fuzzy Cluster Analysis},
      publisher = {John Wiley & Sons, Inc.},
      year = {1999}
    }
    
    Jain, A.K., Murty, M.N. & Flynn, P.J. Data Clustering: A Review 1999 ACM Comput. Surv.
    Vol. 31(3), pp. 264-323 
    article DOI URL 
    Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
    BibTeX:
    @article{Jain:1999:DCR:331499.331504,
      author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
      title = {Data Clustering: A Review},
      journal = {ACM Comput. Surv.},
      publisher = {ACM},
      year = {1999},
      volume = {31},
      number = {3},
      pages = {264--323},
      url = {http://doi.acm.org/10.1145/331499.331504},
      doi = {http://dx.doi.org/10.1145/331499.331504}
    }
    

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