Visualization techniques for mining large databases : A comparison.
IEEE Transactions on Knowledge and Data Engineering, 8(6):923-938, 1996.
D. Keim and H. Kriegel.
[BibTeX]
Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge.
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Wissensmanagement mit Referenzmodellen - Konzepte für die Anwendungssystem- und Organisationsgestaltung, pages 163-174.
Physica, Heidelberg, 2002.
G. Stumme.
[doi]
[BibTeX]
Theorie des kommunikativen Handelns.
1981.
J�rgen Habermas.
[BibTeX]
The Structure of Collaborative Tagging Systems.
Information Dynamics Lab, HP Labs , 2005.
Scott Golder and Bernardo A. Huberman.
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[BibTeX]
The Courseware Watchdog: an Ontology-based tool for finding and organizing
learning material.
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Kassel University Press, 2003.
Julien Tane, Christoph Schmitz, Gerd Stumme, Steffen Staab and R. Studer.
[doi]
[abstract]
[BibTeX]
Topics in education are changing with an ever faster pace. E-Learning
resources tend to be more and more decentralised. Users need increasingly to be able to
use the resources of the web. For this, they should have tools for finding and organizing
information in a decentral way. In this, paper, we show how an ontology-based tool
suite allows to make the most of the resources available on the web.
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research.
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Jonas Poelmans, DmitryI. Ignatov, Stijn Viaene, Guido Dedene and SergeiO. Kuznetsov.
[doi]
[abstract]
[BibTeX]
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
Text Clustering Based on Background Knowledge.
Technical Report , University of Karlsruhe, Institute AIFB, 2003.
Andreas Hotho, Steffen Staab and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.
Set-oriented mining for association rules in relational databases.
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Semantic resource management for the web: an e-learning application.
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[doi]
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Reverse Pivoting in Conceptual Information Systems..
In: H. Delugach and G. Stumme, editors,
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Springer, Heidelberg, 2001.
J. Hereth and G. Stumme.
[doi]
[BibTeX]
Reverse Pivoting in Conceptual Information Systems.
In: H. S. Delugach and G. Stumme, editors,
Conceptual Structures: Broadening the Base, volume 2120, series Lecture Notes in Computer Science, pages 202-215.
Springer, 2001.
J. Hereth and G. Stumme.
[BibTeX]
Relational Scaling and Databases.
In: U. Priss, D. Corbett and G. Angelova, editors,
Conceptual Structures: Integration and Interfaces, 10th International
Conference on Conceptual Structures, ICCS 2002, Borovets, Bulgaria,
July 15-19, 2002, Proceedings, volume 2393, series Lecture Notes in Computer Science, pages 62-76.
Springer, 2002.
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[BibTeX]
Publication Analysis of the Formal Concept Analysis Community.
In: F. Domenach, D. Ignatov and J. Poelmans, editors,
ICFCA 2012, volume 7278, series Lecture Notes in Artificial Intelligence, pages 77-95.
Springer, Berlin/Heidelberg, 2012.
Stephan Doerfel, Robert Jäschke and Gerd Stumme.
[doi]
[abstract]
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We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.
Plädoyer für eine philosophische Grundlegung der Begrifflichen Wissensverarbeitung.
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Philosophisches W�rterbuch.
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On-Line Analytical Processing with Conceptual Information Systems.
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Mining Association Rules in Folksonomies.
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Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.
Mining All Non-derivable Frequent Itemsets.
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data cubes..
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Lattices of Triadic Concept Graphs.
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German Federal Office for Information Security.
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Information Retrieval in Folksonomies: Search and Ranking.
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Springer, 2006.
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[BibTeX]
Incremental concept formation algorithms based on Galois
(concept) lattices.
Computational Intelligence, 11(2):246-267, 1995.
R. Godin, R. Missaoui and H. Alaoui.
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Implementing data cubes efficiently.
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ACM Press, 1996.
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Iceberg Query Lattices for Datalog.
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Springer, Heidelberg, 2004.
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GeoMiner : A system prototype for spatial data mining.
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of Data (SIGMOD'97), pages 553-556.
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AAAI Press, 1996.
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[BibTeX]
Formal Concept Analysis: Mathematical foundations.
1999.
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[BibTeX]
Formal Concept Analysis: Mathematical Foundations.
1999. Translation of: itFormale Begriffsanalyse: Mathematische
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[BibTeX]
Formal Concept Analysis: Foundations and Applications.
LNAI. volume 3626.
Springer, Heidelberg, 2005.
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Formal concept analysis with ConImp : Introduction to the
basic features.
Technische Hochschule Darmstadt, 1998. http://www.mathematik.tu-darmstadt.de/~burmeister/ConImpIntro.ps.
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Formal Concept Analysis on its Way from Mathematics to Computer Science.
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Springer, Heidelberg, 2002.
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Finding interesting rules from large sets of discovered association
rules.
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Knowledge Management (CIKM'94), pages 401-407.
ACM Press, 1994.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A. I. Verkamo.
[BibTeX]
Finding all closed sets : A general approach.
Order, 8:283-290, 1991.
B. Ganter and K. Reuter.
[BibTeX]
Fast algorithms for discovering the maximum frequent sets.
PhD thesis, University of New York, 1998.
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Exploration of the power of attribute oriented induction in data
mining.
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AAAI Press, 1996.
J. Han and Y. Fu.
[BibTeX]
Enhancements to the data mining process.
PhD thesis, University of Stanford, 1997.
G. H. John.
[BibTeX]
Découverte de r�gles pertinentes dans les bases de donn�es.
In:
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1998.
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of Data (SIGMOD'97), pages 255-264.
ACM Press, 1997.
S. Brin, R. Motwani, J. D. Ullman and S. Tsur.
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Document Retrieval for Email Search and Discovery using Formal Concept Analysis.
Journal of Applied Artificial Intelligence (AAI), 17(3):257-280, 2003.
Richard J. Cole, Peter W. Eklund and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
This paper discusses an document discovery tool based on
conceptual clustering by formal concept analysis. The program
allows users to navigate email using a visual lattice metaphor
rather than a tree. It implements a virtual file structure over
email where files and entire directories can appear in multiple
positions. The content and shape of the lattice formed by the
conceptual ontology can assist in email discovery. The system
described provides more flexibility in retrieving stored emails
than what is normally available in email clients. The paper
discusses how conceptual ontologies can leverage traditional
document retrieval systems and aid knowledge discovery in document
collections.
Discovery of spatial association rules in geographic information
databases.
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Databases (SSD'95), series Lecture Notes in Computer Science, Vol. 951, pages 47-66.
Springer-Verlag, 1995.
K. Koperski and J. Han.
[BibTeX]
Discovery of multiple-level association rules from large databases.
In:
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Bases (VLDB'95), pages 420-431.
Morgan Kaufmann, 1995.
J. Han and Y. Fu.
[BibTeX]
Discovering trends in text databases.
In:
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and Data mining (KDD'97), pages 227-230.
AAAI Press, 1997.
B. Lent, R. Agrawal and R. Srikant.
[BibTeX]
Discovering Shared Conceptualizations in Folksonomies.
Journal of Web Semantics, 6(1):38-53, 2008.
Robert Jäschke, Andreas Hotho, Christoph Schmitz, Bernhard Ganter and Gerd Stumme.
[doi]
[BibTeX]
Discovering all most specific sentences by randomized algorithms.
In:
Proceedings of the 6th biennial International Conference on Database
Theory (ICDT'97), series Lecture Notes in Computer Science, Vol. 1186, pages 215-229.
Springer-Verlag, 1997.
D. Gunopulos, H. Mannila and S. Saluja.
[BibTeX]
Design of class hierarchies based on concept (Galois) lattices..
TAPOS, 4(2):117-134, 1998.
R. Godin, H. Mili, G. Mineau, R. Missaoui, A. Arfi and T. Chau.
[BibTeX]
DBMiner : A system for mining knowledge in large relational
databases.
In:
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and Data mining (KDD'96), pages 250-255.
AAAI Press, 1996.
J. Han, Y. Fu, W. Wang, J. Chiang, W. Gong, K. Koperski, D. Li, Y. Lu, A. Rajan, N. Stefanovic, B. Xia and O. R. Zaïane.
[BibTeX]
DBMiner : A system for data mining in relational databases
and data warehouses.
In:
Proceedings of the 1997 GASCON metting of minds (GASCON'97), pages 249-260.
1997.
ftp://ftp.fas.sfu.ca/pub/cs/han/kdd/cascon97.ps
J. Han, J. Chiang, S. Chee, J. Chen, Q. Chen, S. Cheng, W. Gong, M. Kamber, K. Koperski G. Liu, Y. Lu, N. Stefanovic, L. Winstone, B. Xia, O. R. Zaïane, S. Zhang and H. Zhu.
[BibTeX]
Data: Mining: Concepts and Techniques.
2002.
J. Han and M. Kamber.
[BibTeX]
Data-driven discovery of quantitative rules in relational databases.
IEEE Transansaction on Knowledge and Data Engineering, 5(1):29-40, 1993.
J. Han, Y. Cai and N. Cercone.
[BibTeX]
Data Mining: Concepts and Techniques.
2000.
J. Han and M. Kamber.
[BibTeX]
Data Mining: algorithmes par niveau, techniques d'implementation
et applications.
PhD thesis, Université de Clermont-Ferrand II, 2000.
Y. Bastide.
[BibTeX]
Data mining, hypergraph transversals, and machine learning.
In:
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of Database Systems (PODS'97), pages 209-216.
ACM Press, 1997.
D. Gunopulos, R. Khardon, H. Mannila and H. Toivonen.
[BibTeX]
Creation and Merging of Ontology Top-Levels.
In: A. de Moor, W. Lex and B. Ganter, editors,
Conceptual Structures for Knowledge Creation and Communication., volume 2746, series LNAI, pages 131-145.
Springer, Heidelberg, 2003.
Bernhard Ganter and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
We provide a new method for systematically structuring the top-down level of ontologies.
It is based on an interactive, top--down knowledge acquisition
process, which assures that the knowledge engineer
considers all possible cases while avoiding redundant acquisition.
The method is suited especially for creating/merging the top
part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.
Creating a Web Analysis and Visualization Environment.
Computer Networks and ISDN Systems, 28(1&):109-117, 1995.
Robert E. Kent and Christian Neuss.
[doi]
[BibTeX]
Contributions to ICCS 2005.
kassel university press, Kassel, 2005.
Frithjof Dau, Marie-Laure Mugnier and Gerd Stumme.
[doi]
[BibTeX]
Concise Representation of Frequent Patterns Based on Disjunction-Free
Generators.
In:
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Data Mining, pages 305-312.
IEEE Computer Society, Washington, DC, USA, 2001.
Marzena Kryszkiewicz.
[BibTeX]
Conceptual Structures: Common Semantics for Sharing Knowledge, 13th International Conference on Conceptual Structures, ICCS 2005, Kassel, Germany, July 17-22, 2005, Proceedings.
Lecture Notes in Computer Science. volume 3596.
Springer, 2005.
Frithjof Dau, Marie-Laure Mugnier and Gerd Stumme.
[doi]
[BibTeX]
Conceptual Structures - Broadening the Base. Proc. 9th International Conference on Conceptual Structures.
LNAI. volume 2120.
Springer, Heidelberg, 2001.
H. Delugach and G. Stumme.
[BibTeX]
Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies.
In: P. Eklund, editor,
Concept Lattices, volume 2961, series LNAI, pages 189-207.
Springer, Heidelberg, 2004.
Philipp Cimiano, Andreas Hotho, Gerd Stumme and Julien Tane.
[doi]
[abstract]
[BibTeX]
Among many other knowledge representations formalisms, Ontologies
and Formal Concept Analysis (FCA) aim at modeling 'concepts'. We
discuss how these two formalisms may complement another from an
application point of view. In particular, we will see how FCA can
be used to support Ontology Engineering, and how ontologies can be
exploited in FCA applications. The interplay of FCA and ontologies
is studied along the life cycle of an ontology:
(i) FCA can support the building of the ontology as a
learning technique.
(ii) The established ontology can be analyzed and navigated by
using techniques of FCA.
(iii) Last but not least, the ontology may be used to improve an FCA
application.
Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies.
In:
P. Eklund, editor,
Concept Lattices, pages 189-207.
Springer, Berlin/Heidelberg, 2004.
Philipp Cimiano, Andreas Hotho, Gerd Stumme and Julien Tane.
[doi]
[BibTeX]
Conceptual Knowledge Discovery - a Human-Centered Approach.
Journal of Applied Artificial Intelligence (AAI), 17(3):281-301, 2003.
Joachim Hereth, Gerd Stumme, Rudolf Wille and Uta Wille.
[doi]
[abstract]
[BibTeX]
In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge
Processing. Conceptual Knowledge
Processing is based on the mathematical theory of Formal Concept
Analysis which has become a successful theory for data analysis during
the last two decades. CKDD aims to support a human-centered process
of discovering knowledge from data by visualizing and analyzing
the conceptual structure of the data. We dicuss how the
management system TOSCANA for conceptual information systems
supports CKDD, and illustrate it by two applications in database
marketing and flight movement analysis. Finally, we present a
new tool for conceptual deviation discovery, Chianti.
Conceptual Information Systems Discussed Through an IT-Security Tool.
In: R. Dieng and O. Corby, editors,
Knowledge Engineering and Knowledge Management. Methods, Models, and Tools., volume 1937, series LNAI, pages 352-365.
Springer, Heidelberg, 2000.
K. Becker, G. Stumme, R. Wille, U. Wille and M. Zickwolff.
[doi]
[BibTeX]
Computing iceberg concept lattices with TITANIC.
Data & Knowledge Engineering, 42(2):189-222, 2002.
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.
[doi]
[abstract]
[BibTeX]
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.
Comparing performance of algorithms for generating concept lattices.
Journal of Experimental and Theoretical Artificial Intelligence, 14:189-216, 2002.
S. Kuznetsov and S. Obiedkov.
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[BibTeX]
Collaborative tagging as a tripartite network.
2005. ttarXiv:cs.DS/0512090.
R. Lambiotte and M. Ausloos.
[doi]
[abstract]
[BibTeX]
We describe online collaborative communities by tripartite networks,
the nodes being persons, items and tags. We introduce projection
methods in order to uncover the structures of the networks, i.e.
communities of users, genre families... <br />To do so, we focus
on the correlations between the nodes, depending on their profiles,
and use percolation techniques that consist in removing less correlated
links and observing the shaping of disconnected islands. The structuring
of the network is visualised by using a tree representation. The
notion of diversity in the system is also discussed.
Classification and regression trees.
1984.
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Bitmap based algorithms for mining association rules.
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BibSonomy: A Social Bookmark and Publication Sharing System.
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Workshop.
2006.
(to appear)
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[BibTeX]
BibSonomy: A Social Bookmark and Publication Sharing System.
In: A. de Moor, S. Polovina and H. Delugach, editors,
Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, pages 87-102.
Aalborg Universitetsforlag, Aalborg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies and briefly describe
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library.
Beyond market baskets: Generalizing association rules to correlation.
In:
Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97), pages 265-276.
ACM Press, 1997.
S. Brin, R. Motwani and C. Silverstein.
[BibTeX]
Bayesian networks for knowledge discovery.
In:
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, editors,
Advances in Knowledge Discovery and Data Mining, pages 273-305.
AAAI Press, 1996.
D. Heckerman.
[BibTeX]
Attribute Exploration on the Web.
In: P. Cellier, F. Distel and B. Ganter, editors,
Contributions to the 11th International Conference on Formal Concept Analysis, pages 19-34.
2013.
Robert Jäschke and Sebastian Rudolph.
[doi]
[abstract]
[BibTeX]
We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.
Approximation of Frequency Queries by Means of Free-Sets.
In:
PKDD '00: Proceedings of the 4th European Conference on Principles
of Data Mining and Knowledge Discovery, pages 75-85.
Springer-Verlag, London, UK, 2000.
Jean-Francois Boulicaut, Artur Bykowski and Christophe Rigotti.
[BibTeX]
An incremental concept formation approach for learning from databases.
Theoretical Computer Science : Special issue on formal methods in
databases and software engineering, 133(2):387-419, 1994.
R. Godin and R. Missaoui.
[BibTeX]
Aggregate-query processing in data warehousing environments.
In:
Proceedings of the 21st international conference on Very Large Data
Bases (VLDB'95), pages 358-369.
Morgan Kaufmann, 1995.
A. Gupta, V. Harinarayan and D. Quass.
[BibTeX]
Advances in Formal Concept Analysis for Knowledge Discovery in Databases. Proc. Workshop FCAKDD of the 15th European
Conference on Artificial Intelligence (ECAI 2002).
Lyon, France, 2002.
V. Duquenne, B. Ganter, M. Liquiere, E. M. Nguifo and G. Stumme.
[doi]
[BibTeX]
A Logical Generalization of Formal Concept Analysis.
In: G. Mineau and B. Ganter, editors,
Int. Conf. Conceptual Structures, series LNCS 1867, pages 371-384.
Springer, 2000.
S. Ferr� and O. Ridoux.
[abstract]
[BibTeX]
We propose a generalization of Formal Concept Analysis (FCA) in which
sets of attributes are replaced by expressions of an almost arbitrary
logic. We prove that all FCA can be reconstructed on this basis.
We show that from any logic that is used in place of sets of attributes
can be derived a contextualized logic that takes into account the
formal context and that is isomorphic to the concept lattice. We
then justify the generalization of FCA compared with existing extensions
and in the perspective of its application to information systems.
A lattice conceptual clustering system and its application to browsing retrieval.
Machine Learning, 24(2):95-122, 1996.
Claudio Carpineto and Giovanni Romano.
[doi]
[abstract]
[BibTeX]
The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge.
ER -
A Finite State Model for On-Line Analytical Processing in
Triadic Contexts.
In: B. Ganter and R. Godin, editors,
Proc. 3rd Intl. Conf. on Formal Concept Analysis, volume 3403, series Lecture Notes in Computer Science, pages 315-328.
Springer, Heidelberg, 2005.
Gerd Stumme.
[doi]
[BibTeX]
A data-mining methodology and its application to semi-automatic knowledge
acquisition.
In:
Proceedings of the 8th international conference on Database and Expert
systems Applications (DEXA'97), series Lecture Notes in Computer Science, Vol. 1308, pages 670-677.
Springer-Verlag, 1997.
M. Klemettinen, H. Mannila and H. Toivonen.
[BibTeX]
A condensed representation to find frequent patterns..
In:
PODS.
2001.
Artur Bykowski and Christophe Rigotti.
[doi]
[BibTeX]