Artificial Brains:
Fuchs, E.; Gruber, T.; Pree, H. & Sick, B.
(2010):
Temporal data mining using shape space representations of time series.
In: Neurocomputing,
Ausgabe/Number: 1–3,
Vol. 74,
Erscheinungsjahr/Year: 2010.
Seiten/Pages: 379 - 393.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
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.
@article{Fuchs2010379,
author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
title = {Temporal data mining using shape space representations of time series},
journal = {Neurocomputing},
year = {2010},
volume = {74},
number = {1–3},
pages = {379 - 393},
note = {Artificial Brains},
url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
doi = {10.1016/j.neucom.2010.03.022},
issn = {0925-2312},
keywords = {data, everyaware, mining, orthogonal, polynom, polynoms, representations, series, shape, space, temoral, time},
abstract = {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.}
}
%0 = article
%A = Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard
%D = 2010
%T = Temporal data mining using shape space representations of time series
%U = http://www.sciencedirect.com/science/article/pii/S0925231210002237
Lakhal, L. & Stumme, G.
(2005):
Efficient Mining of Association Rules Based on Formal Concept Analysis. LNAI Heidelberg
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Association rules are a popular knowledge discovery technique for
rehouse basket analysis. They indicate which items of the
rehouse are frequently bought together. The problem of association
le mining has first been stated in 1993. Five years later, several
search groups discovered that this problem has a strong connection
Formal Concept Analysis (FCA). In this survey, we will first
troduce some basic ideas of this connection along a specific
gorithm, and show how FCA helps in reducing the number
resulting rules without loss of information, before giving a
neral overview over the history and state of the art of applying
A for association rule mining.
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booktitle = {Formal Concept Analysis: Foundations and Applications},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2005},
volume = {3626},
pages = {180-195},
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abstract = {Association rules are a popular knowledge discovery technique for
rehouse basket analysis. They indicate which items of the
rehouse are frequently bought together. The problem of association
le mining has first been stated in 1993. Five years later, several
search groups discovered that this problem has a strong connection
Formal Concept Analysis (FCA). In this survey, we will first
troduce some basic ideas of this connection along a specific
gorithm, and show how FCA helps in reducing the number
resulting rules without loss of information, before giving a
neral overview over the history and state of the art of applying
A for association rule mining.}
}
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Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.
(2005):
Generating a Condensed Representation for Association Rules.
In: Journal Intelligent Information Systems (JIIS),
Ausgabe/Number: 1,
Vol. 24,
Verlag/Publisher: Kluwer Academic Publishers.
Erscheinungsjahr/Year: 2005.
Seiten/Pages: 29-60.
[Volltext] [BibTeX]
[Endnote]
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author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi},
title = {Generating a Condensed Representation for Association Rules},
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publisher = {Kluwer Academic Publishers},
year = {2005},
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Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
(2002):
Pascal: un alogorithme d'extraction des motifs fréquents.
In: Technique et Science Informatiques (TSI),
Ausgabe/Number: 1,
Vol. 21,
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 65-95.
[Volltext] [BibTeX]
[Endnote]
@article{bastide02unalogorithme,
author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
title = {Pascal: un alogorithme d'extraction des motifs fréquents},
journal = {Technique et Science Informatiques (TSI)},
year = {2002},
volume = {21},
number = {1},
pages = {65-95},
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Stumme, G.
(2002):
Efficient Data Mining Based on Formal Concept Analysis.
In: Database and Expert Systems Applications. Proc. DEXA 2002,
Heidelberg.
[Volltext]
[BibTeX][Endnote]
@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 = {2002, association, closed, condensed, data, discovery, fca, itemsets, kdd, knowledge, mining, myown, representations, rules}
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%A = Stumme, G.
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%C = Heidelberg
%D = 2002
%I = Springer
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Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2001):
Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis.
In: KI 2001: Advances in Artificial Intelligence. KI 2001,
Heidelberg.
[Volltext]
[BibTeX][Endnote]
@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},
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pages = {335-350},
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%C = Heidelberg
%D = 2001
%I = Springer
%T = Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis
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Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
(2000):
Levelwise Search of Frequent Patterns.
In: Actes des 16ièmes Journées Bases de Données Avancées,
France.
[Volltext]
[BibTeX][Endnote]
@inproceedings{bastide00levelwise,
author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
title = {Levelwise Search of Frequent Patterns},
booktitle = {Actes des 16ièmes Journées Bases de Données Avancées},
publisher = {Blois},
address = {France},
year = {2000},
pages = {307-322},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/BDA00.pdf},
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%0 = inproceedings
%A = Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.
%B = Actes des 16ièmes Journées Bases de Données Avancées
%C = France
%D = 2000
%I = Blois
%T = Levelwise Search of Frequent Patterns
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2000/BDA00.pdf
Bastide, Y.; Taouil, R.; Pasquier, N.; Stumme, G. & Lakhal, L.
(2000):
Mining Frequent Patterns with Counting Inference..
In: SIGKDD Explorations, Special Issue on Scalable Algorithms,
Ausgabe/Number: 2,
Vol. 2,
Erscheinungsjahr/Year: 2000.
Seiten/Pages: 71-80.
[BibTeX]
[Endnote]
@article{bastide00miningfrequent,
author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
title = {Mining Frequent Patterns with Counting Inference.},
journal = {SIGKDD Explorations, Special Issue on Scalable Algorithms},
year = {2000},
volume = {2},
number = {2},
pages = {71-80},
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%0 = article
%A = Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.
%D = 2000
%T = Mining Frequent Patterns with Counting Inference.
Bastide, Y.; Pasquier, N.; Taouil, R.; Stumme, G. & Lakhal, L.
(2000):
Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets.
In: Computational Logic -- CL 2000 Proc. CL'00,
Heidelberg.
[Volltext]
[BibTeX][Endnote]
@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},
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%C = Heidelberg
%D = 2000
%I = Springer
%T = Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets
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http://ceur-ws.org/Vol-29. Part of testumme02computing:
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2000):
Fast Computation of Concept Lattices Using Data Mining Techniques.
In: Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases,
[Volltext]
[BibTeX][Endnote]
@inproceedings{stumme00fast,
author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.},
title = {Fast Computation of Concept Lattices Using Data Mining Techniques},
editor = {Bouzeghoub, M. and Klusch, M. and Nutt, W. and Sattler, U.},
booktitle = {Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases},
year = {2000},
note = {http://ceur-ws.org/Vol-29. Part of testumme02computing},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf},
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}
%0 = inproceedings
%A = Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.
%B = Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases
%D = 2000
%T = Fast Computation of Concept Lattices Using Data Mining Techniques
%U = http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf
Stumme, G.
(1999):
Conceptual Knowledge Discovery with Frequent Concept Lattices.
[Volltext] [BibTeX]
[Endnote]
@techreport{stumme99conceptualknowledge,
author = {Stumme, G.},
title = {Conceptual Knowledge Discovery with Frequent Concept Lattices},
type = {FB4-Preprint 2043},
year = {1999},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf},
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%D = 1999
%T = Conceptual Knowledge Discovery with Frequent Concept Lattices
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