Temporal data mining using shape space representations of time series.
Neurocomputing, 74(1–3):379 - 393, 2010.
Artificial Brains
Erich Fuchs, Thiemo Gruber, Helmuth Pree und Bernhard Sick.
[doi]
[Kurzfassung]
[BibTeX]
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.
Efficient Mining of Association Rules Based on Formal Concept Analysis.
2005.
Lotfi Lakhal und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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, 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.
Generating a Condensed Representation for Association Rules.
Journal Intelligent Information Systems (JIIS), 24(1):29-60, 2005.
Nicolas Pasquier, Rafik Taouil, Yves Bastide, Gerd Stumme und Lotfi Lakhal.
[doi]
[BibTeX]
Pascal: un alogorithme d'extraction des motifs fréquents.
Technique et Science Informatiques (TSI), 21(1):65-95, 2002.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme und L. Lakhal.
[doi]
[BibTeX]
Efficient Data Mining Based on Formal Concept Analysis.
In: A. Hameurlain, R. Cicchetti und R. Traunmüller
(Herausgeber):
Database and Expert Systems Applications. Proc. DEXA 2002, Band 2453, Reihe LNCS, Seiten 534-546.
Springer, Heidelberg, 2002.
G. Stumme.
[doi]
[BibTeX]
Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis.
In: F. Baader, G. Brewker und T. Eiter
(Herausgeber):
KI 2001: Advances in Artificial Intelligence. KI 2001, Band 2174, Reihe LNAI, Seiten 335-350.
Springer, Heidelberg, 2001.
G. Stumme, R. Taouil, Y. Bastide, N. Pasquier und L. Lakhal.
[doi]
[BibTeX]
Levelwise Search of Frequent Patterns.
In:
Actes des 16ièmes Journées Bases de Données Avancées, Seiten 307-322.
Blois, France, 2000.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme und L. Lakhal.
[doi]
[BibTeX]
Mining Frequent Patterns with Counting Inference..
SIGKDD Explorations, Special Issue on Scalable Algorithms, 2(2):71-80, 2000.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme und L. Lakhal.
[BibTeX]
Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets.
In: J. Lloyd, V. Dahl, U. Furbach, M. Kerber, K.-K. Laus, C. Palamidessi, L. Pereira, Y. Sagiv und P. Stuckey
(Herausgeber):
Computational Logic -- CL 2000 Proc. CL'00, Band 1861, Reihe LNAI.
Springer, Heidelberg, 2000.
Y. Bastide, N. Pasquier, R. Taouil, G. Stumme und L. Lakhal.
[doi]
[BibTeX]
Fast Computation of Concept Lattices Using Data Mining Techniques.
In: M. Bouzeghoub, M. Klusch, W. Nutt und U. Sattler
(Herausgeber):
Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases.
2000.
http://ceur-ws.org/Vol-29. Part of testumme02computing
G. Stumme, R. Taouil, Y. Bastide, N. Pasquier und L. Lakhal.
[doi]
[BibTeX]
Conceptual Knowledge Discovery with Frequent Concept Lattices.
FB4-Preprint 2043, TU Darmstadt, 1999.
G. Stumme.
[doi]
[BibTeX]