%0 
%0 Journal Article
%A Fuchs, Erich; Gruber, Thiemo; Pree, Helmuth & Sick, Bernhard
%D 2010
%T Temporal data mining using shape space representations of time series
%E 
%B Neurocomputing
%C 
%I 
%V 74
%6
%N 1–3
%P 379 - 393
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%2 ScienceDirect.com - Neurocomputing - Temporal data mining using shape space representations of time series
%3 article
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%F Fuchs2010379
%K data, everyaware, mining, orthogonal, polynom, polynoms, representations, series, shape, space, temoral, time
%X Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.
%Z Artificial Brains
%U http://www.sciencedirect.com/science/article/pii/S0925231210002237
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%0 
%0 Book Section
%A Lakhal, Lotfi & Stumme, Gerd
%D 2005
%T Efficient Mining of Association Rules Based on Formal Concept Analysis
%E Ganter, Bernhard; Stumme, Gerd & Wille, Rudolf
%B Formal Concept Analysis: Foundations and Applications
%C Heidelberg
%I Springer
%V 3626
%6
%N 
%P 180-195
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%S LNAI
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%F lakhal2005efficient
%K 2005, analysis, association, book, closed, concept, condensed, data, discovery, fca, formal, itegpub, itemsets, kdd, knowledge, l3s, mining, myown, representations, rules
%X Association rules are a popular knowledge discovery technique for
warehouse basket analysis. They indicate which items of the
warehouse are frequently bought together. The problem of association
rule mining has first been stated in 1993. Five years later, several
research groups discovered that this problem has a strong connection
to Formal Concept Analysis (FCA). In this survey, we will first
introduce some basic ideas of this connection along a specific
algorithm, \titanic, and show how FCA helps in reducing the number
of resulting rules without loss of information, before giving a
general overview over the history and state of the art of applying
FCA for association rule mining.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf
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%0 Journal Article
%A Pasquier, Nicolas; Taouil, Rafik; Bastide, Yves; Stumme, Gerd & Lakhal, Lotfi
%D 2005
%T Generating a Condensed Representation for Association Rules
%E 
%B Journal Intelligent Information Systems (JIIS)
%C 
%I Kluwer Academic Publishers
%V 24
%6
%N 1
%P 29-60
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%F pasquier2005generating
%K 2005, OntologyHandbook, analysis, association, closed, concept, condensed, data, discovery, fca, formal, itegpub, itemset, kdd, knowledge, l3s, mining, myown, representations, rule, rules, sets
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf
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%0 
%0 Journal Article
%A Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
%D 2002
%T Pascal: un alogorithme d'extraction des motifs fréquents
%E 
%B Technique et Science Informatiques (TSI)
%C 
%I 
%V 21
%6
%N 1
%P 65-95
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%F bastide02unalogorithme
%K 2002, analysis, association, closed, concept, condensed, fca, formal, iceberg, itemsets, lattices, myown, pascal, representations, rules, titanic
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2001/TSI01.pdf
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%0 
%0 Conference Proceedings
%A Stumme, G.
%D 2002
%T Efficient Data Mining Based on Formal Concept Analysis
%E Hameurlain, A.; Cicchetti, R. & Traunmüller, R.
%B Database and Expert Systems Applications. Proc. DEXA 2002
%C Heidelberg
%I Springer
%V 2453
%6
%N 
%P 534-546
%& 
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%S LNCS
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%F stumme02efficient
%K 2002, association, closed, condensed, data, discovery, fca, itemsets, kdd, knowledge, mining, myown, representations, rules
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2002/DEXA02.pdf
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%0 
%0 Conference Proceedings
%A Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
%D 2001
%T Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis
%E Baader, F.; Brewker, G. & Eiter, T.
%B KI 2001: Advances in Artificial Intelligence. KI 2001
%C Heidelberg
%I Springer
%V 2174
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%P 335-350
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%F stumme01intelligent
%K 2001, FCA, OntologyHandbook, analysis, association, bases, closed, concept, condensed, discovery, fca, formal, itemsets, kdd, knowledge, mining, myown, representations, rule, rules
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2001/KI01.pdf
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%0 Conference Proceedings
%A Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
%D 2000
%T Levelwise Search of Frequent Patterns
%E 
%B Actes des 16ièmes Journées Bases de Données Avancées
%C France
%I Blois
%V 
%6
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%P 307-322
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%8 Oct 24-27
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%K 2000, algorithm, algorithms, analysis, association, closed, concept, condensed, data, discovery, fca, formal, frequent, itemsets, kdd, knowledge, mining, myown, representations, rules
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/BDA00.pdf
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%0 Journal Article
%A Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
%D 2000
%T Mining Frequent Patterns with Counting Inference.
%E 
%B SIGKDD Explorations, Special Issue on Scalable Algorithms
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%V 2
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%N 2
%P 71-80
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%K 2000, FCA, OntologyHandbook, analys, association, closed, concept, condensed, data, discovery, fca, formal, frequent, itemsets, kdd, knowledge, mining, myown, representation, representations, rule, rules
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%0 Conference Proceedings
%A Bastide, Y.; Pasquier, N.; Taouil, R.; Stumme, G. & Lakhal, L.
%D 2000
%T Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets
%E Lloyd, J.; Dahl, V.; Furbach, U.; Kerber, M.; Laus, K.-K.; Palamidessi, C.; Pereira, L.M.; Sagiv, Y. & Stuckey, P.J.
%B Computational Logic --- CL 2000 Proc. CL'00
%C Heidelberg
%I Springer
%V 1861
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%F bastide00miningminimal
%K 2000, analys, association, closed, concept, condensed, data, discovery, fca, formal, frequent, itemsets, kdd, knowledge, mining, myown, representation, representations, rule, rules
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/DOOD00.pdf
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%0 Conference Proceedings
%A Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
%D 2000
%T Fast Computation of Concept Lattices Using Data Mining Techniques
%E Bouzeghoub, M.; Klusch, M.; Nutt, W. & Sattler, U.
%B Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases
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%F stumme00fast
%K 2000, algorithm, algorithms, analysis, closed, computation, concept, condensed, fca, formal, iceberg, itemsets, lattices, myown, representations
%X 
%Z \url{http://ceur-ws.org/Vol-29.} {P}art of \cite{stumme02computing}
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf
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%0 
%0 Report
%A Stumme, G.
%D 1999
%T Conceptual Knowledge Discovery with Frequent Concept Lattices
%E 
%B 
%C 
%I TU Darmstadt
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%K 1999, analysis, association, closed, concept, condensed, data, discovery, fca, formal, frequent, iceberg, itemsets, kdd, knowledge, lattices, mining, myown, representations, rule, rules
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf
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