Cerf, Loïc and Besson, Jérémy and Robardet, Céline and Boulicaut, Jean-François
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data
ACM
2009
3
1–36
1
New York, NY, USA
Set pattern discovery from binary relations has been extensively studied during the last decade. In particular, many complete and efficient algorithms for frequent closed set mining are now available. Generalizing such a task to n-ary relations (n ≥ 2) appears as a timely challenge. It may be important for many applications, for example, when adding the time dimension to the popular objects × features binary case. The generality of the task (no assumption being made on the relation arity or on the size of its attribute domains) makes it computationally challenging. We introduce an algorithm called Data-Peeler. From an n-ary relation, it extracts all closed n-sets satisfying given piecewise (anti) monotonic constraints. This new class of constraints generalizes both monotonic and antimonotonic constraints. Considering the special case of ternary relations, Data-Peeler outperforms the state-of-the-art algorithms CubeMiner and Trias by orders of magnitude. These good performances must be granted to a new clever enumeration strategy allowing to efficiently enforce the closeness property. The relevance of the extracted closed n-sets is assessed on real-life 3-and 4-ary relations. Beyond natural 3-or 4-ary relations, expanding a relation with an additional attribute can help in enforcing rather abstract constraints such as the robustness with respect to binarization. Furthermore, a collection of closed n-sets is shown to be an excellent starting point to compute a tiling of the dataset.
10.1145/1497577.1497580
ol_web2.0, triadic, trias, three, mode, fca
Cerf, Loïc and Besson, Jérémy and Robardet, Céline and Boulicaut, Jean-Francois
Data-Peeler: Constraint-based Closed Pattern Mining in n-ary Relations
Proc. SIAM International Conference on Data Mining SDM'08
2008
April
37–48
Set pattern discovery from binary relations has been extensively studied during the last decade. In particular, many complete and efficient algorithms which extract frequent closed sets are now available. Generalizing such a task to n-ary relations (n ≥ 2) appears as a timely challenge. It may be important for many applications, e.g., when adding the time dimension to the popular objects × features binary case. The generality of the task — no assumption being made on the relation arity or on the size of its attribute domains — makes it computationally challenging. We introduce an algorithm called Data-Peeler. From a n-ary relation, it extracts all closed n-sets satisfying given piecewise (anti)-monotonic constraints. This new class of constraints generalizes both monotonic and anti-monotonic constraints. Considering the special case of ternary relations, Data-Peeler outperforms the state-of-the-art algorithms CubeMiner and Trias by orders of magnitude. These good performances must be granted to a new clever enumeration strategy allowing an efficient closeness checking. An original application on a real-life 4-ary relation is used to assess the relevancy of closed n-sets constraint-based mining.
http://www.siam.org/proceedings/datamining/2008/dm08_04_Cerf.pdf
ol_web2.0, triadic, trias, three, mode, fca
Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd
Hoche, Susanne and Nürnberger, Andreas and Flach, Jürgen
Network Properties of Folksonomies
AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''
IOS Press
2007
20
245-262
4
http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf
2007, semantics, emergent, folksonomy, tagorapub, folksonomies, l3s, network, itegpub, fca
See http://www.bibsonomy.org/bibtex/2e1a5234a896b1f422473b1fe5d91e26b/stumme for a shorter workshop version.
Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd
Priss, U. and Polovina, S. and Hill, R.
Analysis of the Publication Sharing Behaviour in BibSonomy
Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)
Springer-Verlag
2007
4604
July
283–295
Lecture Notes in Artificial Intelligence
Berlin, Heidelberg
BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.
2007, analysis, bibsonomy, bookmarking, fca, folksonomy, iccs, l3s, ol_web2.0, social, trias, widely_related, emergentsemantics_evidence
Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd
de Moor, Aldo and Polovina, Simon and Delugach, Harry
BibSonomy: A Social Bookmark and Publication Sharing System
Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures
Aalborg Universitetsforlag
2006
87-102
Aalborg
Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarksand publication references in a kind of personal library.
http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf
2006, bookmarking, social, nepomuk, bibsonomy, OntologyHandbook, FCA, folksonomy, tagorapub, iccs, l3s
Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)
IEEE Computer Society
2006
December
907-911
Hong Kong
In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.
http://www.kde.cs.uni-kassel.de/jaeschke/paper/jaeschke06trias.pdf
http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162
12, 2006, algorithm, fca, iccs_example, l3s, ol_web2.0, trias, trias_example, methods_concepts
Cimiano, P. and Hotho, A. and Staab, S.
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis
Journal of Artificial Intelligence Research
2005
24
305-339
1
http://ontology.csse.uwa.edu.au/reference/browse_paper.php?pid=233281549
fca, ol_web2.0, ontology_learning, taxonomy_learning