Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
@book{balbymarinho2012recommender,
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
title = {Recommender Systems for Social Tagging Systems},
series = {SpringerBriefs in Electrical and Computer Engineering},
publisher = {Springer},
year = {2012},
url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
doi = {10.1007/978-1-4614-1894-8},
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keywords = {recommender, social_tagging, baarbeit, toread},
abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}
}
Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
@book{balbymarinho2012recommender,
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
title = {Recommender Systems for Social Tagging Systems},
series = {SpringerBriefs in Electrical and Computer Engineering},
publisher = {Springer},
year = {2012},
url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
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abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}
}
Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
@book{balbymarinho2012recommender,
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
title = {Recommender Systems for Social Tagging Systems},
series = {SpringerBriefs in Electrical and Computer Engineering},
publisher = {Springer},
year = {2012},
url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1},
isbn = {978-1-4614-1893-1},
keywords = {tagging, recommender, collaborative, social, folksonomy, bookmarking, myown, 2012},
abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}
}
Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer, 2012SpringerBriefs in Electrical and Computer Engineering
[Volltext]
Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
@book{balbymarinho2012recommender,
author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
title = {Recommender Systems for Social Tagging Systems},
series = {SpringerBriefs in Electrical and Computer Engineering},
publisher = {Springer},
year = {2012},
url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
doi = {10.1007/978-1-4614-1894-8},
isbn = {978-1-4614-1893-1},
keywords = {tagging, recommender, collaborative, social, folksonomy, bookmarking, myown, 2012},
abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}
}
Mitzlaff, F. & Stumme, G.: Relatedness of given names. In: Human Journal 1 (2012), Nr. 4, S. 205-217
@article{mitzlaff2012relatedness,
author = {Mitzlaff, Folke and Stumme, G},
title = {Relatedness of given names},
journal = {Human Journal},
year = {2012},
volume = {1},
number = {4},
pages = {205--217},
keywords = {relatedness, similarity, nameling, cosine}
}
Benz, D.; Hotho, A.; Jaschke, R.; Krause, B.; Mitzlaff, F.; Schmitz, C. & Stumme, G.: The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research. In: VLDB JOURNAL 19 (2010), Nr. 6,
Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.
@article{benz2010social,
author = {Benz, D and Hotho, A and Jaschke, R and Krause, B and Mitzlaff, F and Schmitz, C and Stumme, G},
title = {The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research},
journal = {VLDB JOURNAL},
year = {2010},
volume = {19},
number = {6},
keywords = {bibsonomy},
abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.}
}
Benz, D.; Hotho, A.; Jaschke, R.; Stumme, G.; Halle, A.; Lima, A. G. S.; Steenweg, H.; Stefani, S.; Lalmas, M.; Jose, J.; Rauber, A.; Sebastiani, F. & Frommholz, I.: Academic Publication Management with PUMA - Collect, Organize and Share Publications. In: LECTURE NOTES IN COMPUTER SCIENCE (2010), Nr. 6273,
@article{benz2010academic,
author = {Benz, D. and Hotho, A. and Jaschke, R. and Stumme, G. and Halle, A. and Lima, A. G. S. and Steenweg, H. and Stefani, S. and Lalmas, M. and Jose, J. and Rauber, A. and Sebastiani, F. and Frommholz, I.},
title = {Academic Publication Management with PUMA - Collect, Organize and Share Publications},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
year = {2010},
number = {6273},
keywords = {PUMA}
}
Benz, D.; Hotho, A.; Jaschke, R.; Stumme, G.; Halle, A.; Lima, A. G. S.; Steenweg, H.; Stefani, S.; Lalmas, M.; Jose, J.; Rauber, A.; Sebastiani, F. & Frommholz, I.: Academic Publication Management with PUMA - Collect, Organize and Share Publications. In: LECTURE NOTES IN COMPUTER SCIENCE (2010), Nr. 6273,
@article{benz2010academic,
author = {Benz, D. and Hotho, A. and Jaschke, R. and Stumme, G. and Halle, A. and Lima, A. G. S. and Steenweg, H. and Stefani, S. and Lalmas, M. and Jose, J. and Rauber, A. and Sebastiani, F. and Frommholz, I.},
title = {Academic Publication Management with PUMA - Collect, Organize and Share Publications},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
year = {2010},
number = {6273},
keywords = {PUMA}
}
Cattuto, C.; Schmitz, C.; Baldassarri, A.; Servedio, V. D. P.; Loreto, V.; Hotho, A.; Grahl, M. & Stumme, G.: Network Properties of Folksonomies. In: AI Communications 20 (2007), Nr. 4, S. 245 - 262
[Volltext]
@article{cattuto2007,
author = {Cattuto, C. and Schmitz, C. and Baldassarri, A. and Servedio, V. D. P. and Loreto, V. and Hotho, A. and Grahl, M. and Stumme, G.},
title = {Network Properties of Folksonomies},
journal = {AI Communications},
year = {2007},
volume = {20},
number = {4},
pages = {245 - 262},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf},
keywords = {clustering, folksonomy, community}
}
Hotho, A.; Staab, S. & Stumme, G.: Wordnet improves text document clustering. Proc. SIGIR Semantic Web Workshop. Toronto: 2003
[Volltext]
@inproceedings{hotho03wordnet,
author = {Hotho, A and Staab, S. and Stumme, G.},
title = {Wordnet improves text document clustering},
booktitle = {Proc. SIGIR Semantic Web Workshop},
address = {Toronto},
year = {2003},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003wordnet.pdf},
keywords = {information, 2003, discovery, text, ir, kmeans, kdd, retrieval, myown, data, knowledge, document, clustering, mining, wordnet}
}
Gonzalez-Olalla, J. & Stumme, G.: Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description). In: Berendt, B.; Hotho, A. & Stumme, G. (Hrsg.): Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf.. Helsinki: 2002, S. 90
[Volltext]
@inproceedings{gonzalez02semantic,
author = {Gonzalez-Olalla, J. and Stumme, G.},
title = {Semantic Methods and Tools for Information Portals - The SemIPort Project (Project Description)},
editor = {Berendt, B. and Hotho, A. and Stumme, G.},
booktitle = {Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf.},
address = {Helsinki},
year = {2002},
pages = {90},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/gonzalez2002semantic.pdf},
keywords = {informationsportale, information, project, Semantic, ontologies, semiport, portals, semantic, 2002, bmbf, myown, portal, web}
}
Schmitz, C.; Staab, S.; Studer, R.; Stumme, G. & Tane, J.: Accessing Distributed Learning Repositories through a Courseware
Watchdog. In: Driscoll, M. & Reeves, T. (Hrsg.): Proc. of E-Learning 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education on (E-Learning 2002). Norfolk: 2002 (AACE), S. 909-915
[Volltext]
@inproceedings{schmitz02accessing,
author = {Schmitz, C. and Staab, S. and Studer, R. and Stumme, G. and Tane, J.},
title = {Accessing Distributed Learning Repositories through a Courseware
Watchdog},
editor = {Driscoll, M. and Reeves, T.C.},
booktitle = {Proc. of E-Learning 2002 World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education on (E-Learning 2002)},
address = {Norfolk},
year = {2002},
volume = {AACE},
pages = {909-915},
note = {Awarded paper},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/E-Learn02.pdf},
keywords = {ontologies, OntologyHandbook, courseware, semantic, crawler, myown, watchdog, web, p2p, 2002, FCA, fca, edutella}
}
Stumme, G.: Efficient Data Mining Based on Formal Concept Analysis. In: Hameurlain, A.; Cicchetti, R. & Traunmüller, R. (Hrsg.): Database and Expert Systems Applications. Proc. DEXA 2002. Heidelberg: Springer, 2002 (LNCS 2453), S. 534-546
[Volltext]
@inproceedings{stumme02efficient,
author = {Stumme, G.},
title = {Efficient Data Mining Based on Formal Concept Analysis},
editor = {Hameurlain, A. and Cicchetti, R. and Traunmüller, R.},
booktitle = {Database and Expert Systems Applications. Proc. DEXA 2002},
series = {LNCS},
publisher = {Springer},
address = {Heidelberg},
year = {2002},
volume = {2453},
pages = {534-546},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/DEXA02.pdf},
keywords = {discovery, association, kdd, myown, data, knowledge, rules, closed, condensed, 2002, itemsets, fca, representations, mining}
}
Stumme, G.; Berendt, B. & Hotho, A.: Usage Mining for and on the Semantic Web. Proc. NSF Workshop on Next Generation Data Mining. Baltimore: 2002, S. 77-86
[Volltext]
@inproceedings{stumme02usage,
author = {Stumme, G. and Berendt, B. and Hotho, A.},
title = {Usage Mining for and on the Semantic Web},
booktitle = {Proc. NSF Workshop on Next Generation Data Mining},
address = {Baltimore},
year = {2002},
pages = {77-86},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/NSF-NGDM02.pdf},
keywords = {semantic, 2002, myown, mining, usage, web}
}
Hereth, J. & Stumme, G.: Reverse Pivoting in Conceptual Information Systems. In: Delugach, H. S. & Stumme, G. (Hrsg.): Conceptual Structures: Broadening the Base. Springer, 2001 (Lecture Notes in Computer Science 2120), S. 202-215
@inproceedings{HerethStumme01Reverse,
author = {Hereth, J. and Stumme, G.},
title = {Reverse Pivoting in Conceptual Information Systems},
editor = {Delugach, H. S. and Stumme, G.},
booktitle = {Conceptual Structures: Broadening the Base},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {2001},
volume = {2120},
pages = {202-215},
isbn = {3-540-42344-3},
keywords = {OntologyHandbook, FCA}
}
Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L.: Conceptual Clustering with Iceberg Concept Lattices. In: Klinkenberg, R.; Rüping, S.; Fick, A.; Henze, N.; Herzog, C.; Molitor, R. & Schröder, O. (Hrsg.): Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01). Universität Dortmund 763: 2001
[Volltext]
@inproceedings{stumme01conceptualclustering,
author = {Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L.},
title = {Conceptual Clustering with Iceberg Concept Lattices},
editor = {Klinkenberg, R. and Rüping, S. and Fick, A. and Henze, N. and Herzog, C. and Molitor, R. and Schröder, O.},
booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)},
address = {Universität Dortmund 763},
year = {2001},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf},
keywords = {concept, iceberg, discovery, analysis, kdd, lattices, knowledge, formal, closed, conceptual, clustering, itemsets, 2001, fca}
}
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.: Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis. In: Baader, F.; Brewker, G. & Eiter, T. (Hrsg.): KI 2001: Advances in Artificial Intelligence. KI 2001. Heidelberg: Springer, 2001 (LNAI 2174), S. 335-350
[Volltext]
@inproceedings{stumme01intelligent,
author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.},
title = {Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis},
editor = {Baader, F. and Brewker, G. and Eiter, T.},
booktitle = {KI 2001: Advances in Artificial Intelligence. KI 2001},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2001},
volume = {2174},
pages = {335-350},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/KI01.pdf},
keywords = {concept, discovery, association, OntologyHandbook, bases, analysis, kdd, myown, rule, knowledge, closed, formal, rules, condensed, FCA, itemsets, 2001, fca, representations, mining}
}
Bastide, Y.; Pasquier, N.; Taouil, R.; Stumme, G. & Lakhal, L.: Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets. In: Lloyd, J.; Dahl, V.; Furbach, U.; Kerber, M.; Laus, K.-K.; Palamidessi, C.; Pereira, L.; Sagiv, Y. & Stuckey, P. (Hrsg.): Computational Logic -- CL 2000 Proc. CL'00. Heidelberg: Springer, 2000 (LNAI 1861)
[Volltext]
@inproceedings{bastide00miningminimal,
author = {Bastide, Y. and Pasquier, N. and Taouil, R. and Stumme, G. and Lakhal, L.},
title = {Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets},
editor = {Lloyd, J. and Dahl, V. and Furbach, U. and Kerber, M. and Laus, K.-K. and Palamidessi, C. and Pereira, L.M. and Sagiv, Y. and Stuckey, P.J.},
booktitle = {Computational Logic --- CL 2000 Proc. CL'00},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2000},
volume = {1861},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/DOOD00.pdf},
keywords = {concept, discovery, analys, association, kdd, myown, frequent, rule, data, knowledge, closed, formal, rules, representation, condensed, itemsets, fca, 2000, representations, mining}
}
Prediger, S. & Stumme, G.: Theory-Driven Logical Scaling. In: et al, E. F. (Hrsg.): Proc. 6th Intl. Workshop Knowledge Representation Meets Databases (KRDB'99). 1999 (CEUR Workshop Proc. 21)
[Volltext]
@inproceedings{prediger99theory,
author = {Prediger, S. and Stumme, G.},
title = {Theory-Driven Logical Scaling},
editor = {et al, E. Franconi},
booktitle = {Proc. 6th Intl. Workshop Knowledge Representation Meets Databases (KRDB'99)},
year = {1999},
volume = {CEUR Workshop Proc. 21},
note = {Also in: P. Lambrix et al (Eds.): Proc. Intl. Workshop on Description Logics (DL'99). CEUR Workshop Proc. 22, 1999
http://ceur-ws.org/Vol-21},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/KRDB99.pdf},
keywords = {1999, conceptual, concept, formal, analysis, fca, myown, scaling, lattices}
}
Stumme, G. & Wille, R.: A geometrical heuristic for drawing concept lattices. In: Tamassia, R. & Tollis, I. (Hrsg.): Graph Drawing. Springer-Verlag, 1995Lecture Notes in Computer Science, Vol. 894 , S. 452-459
@incollection{Stumme95,
author = {Stumme, G. and Wille, R.},
title = {A geometrical heuristic for drawing concept lattices},
editor = {Tamassia, R. and Tollis, I.G.},
booktitle = {Graph Drawing},
series = {Lecture Notes in Computer Science, Vol. 894},
publisher = {Springer-Verlag},
year = {1995},
pages = {452--459},
keywords = {1995, OntologyHandbook, FCA, myown}
}