@article{Fuchs2010379,
  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.},
  author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
  doi = {10.1016/j.neucom.2010.03.022},
  interhash = {88c499ac1dc9e9708e70187967494219},
  intrahash = {fdf6865c1bece3f77cc3e29365a2c6b3},
  issn = {0925-2312},
  journal = {Neurocomputing},
  note = {Artificial Brains},
  number = {1–3},
  pages = {379 - 393},
  title = {Temporal data mining using shape space representations of time series},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
  volume = 74,
  year = 2010
}

@article{bastide02unalogorithme,
  author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
  comment = {alpha},
  interhash = {57813a40a2892bdbdae79116ce2f9d5a},
  intrahash = {494ab134ddccac2af4a1d4a7cb52a78d},
  journal = {Technique et Science Informatiques (TSI)},
  number = 1,
  pages = {65-95},
  title = {Pascal: un alogorithme d'extraction des motifs fréquents},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/TSI01.pdf},
  volume = 21,
  year = 2002
}

@inproceedings{stumme00fast,
  author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasquier, N. and Lakhal, L.},
  booktitle = {Proc. 7th Intl. Workshop on Knowledge Representation Meets Databases},
  comment = {alpha},
  editor = {Bouzeghoub, M. and Klusch, M. and Nutt, W. and Sattler, U.},
  interhash = {12f70b6e4c9bd5fbbec7aea5aba76a89},
  intrahash = {dcfad94fb256027fbe41150d5ca35d5f},
  note = {\url{http://ceur-ws.org/Vol-29.} {P}art of \cite{stumme02computing}},
  title = {Fast Computation of Concept Lattices Using Data Mining Techniques},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/KRDB00.pdf},
  year = 2000
}

@techreport{stumme99conceptualknowledge,
  author = {Stumme, G.},
  comment = {alpha},
  institution = {TU Darmstadt},
  interhash = {c33970150f97bad7972281e38b42738f},
  intrahash = {6d562dc043ba698acee8a83ce35bde6e},
  title = {Conceptual Knowledge Discovery with Frequent Concept Lattices},
  type = {{FB}4-{P}reprint 2043},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf},
  year = 1999
}

@inproceedings{bastide00levelwise,
  address = {France},
  author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
  booktitle = {Actes des 16ièmes Journées Bases de Données Avancées},
  comment = {alpha},
  interhash = {a3181cc73b190099592107cf465c4e43},
  intrahash = {a11de6a74851c7076452159d1b12489b},
  month = {Oct 24-27},
  pages = {307-322},
  publisher = {Blois},
  title = {Levelwise Search of Frequent Patterns},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/BDA00.pdf},
  year = 2000
}

@article{pasquier2005generating,
  author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi},
  comment = {alpha},
  interhash = {cb0ee99fae39f2a5e0af5be9d97978f5},
  intrahash = {40f59a7fa7ce5015f9ee81709db89de0},
  journal = {Journal Intelligent Information Systems (JIIS)},
  number = 1,
  pages = {29-60},
  publisher = {Kluwer Academic Publishers},
  title = {Generating a Condensed Representation for Association Rules},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf},
  volume = 24,
  year = 2005
}

@inbook{lakhal2005efficient,
  abstract = {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.},
  address = {Heidelberg},
  author = {Lakhal, Lotfi and Stumme, Gerd},
  booktitle = {Formal Concept Analysis: Foundations and Applications},
  editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf},
  ee = {http://dx.doi.org/10.1007/11528784_10},
  interhash = {f5777a0f9dccfcf4f9968119d77297fc},
  intrahash = {2b350f817428e4c6c7259cd279815091},
  pages = {180-195},
  publisher = {Springer},
  series = {LNAI},
  title = {Efficient Mining of Association Rules Based on Formal Concept Analysis},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf},
  volume = 3626,
  year = 2005
}

@inproceedings{stumme02efficient,
  address = {Heidelberg},
  author = {Stumme, G.},
  booktitle = {Database and Expert Systems Applications. Proc. DEXA 2002},
  editor = {Hameurlain, A. and Cicchetti, R. and Traunmüller, R.},
  interhash = {56611a15d60e2711a0aafc257715c03d},
  intrahash = {0adce6a0db24566bb55d6e2d6667c8e7},
  pages = {534-546},
  publisher = {Springer},
  series = {LNCS},
  title = {Efficient Data Mining Based on Formal Concept Analysis},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/DEXA02.pdf},
  volume = 2453,
  year = 2002
}

@inproceedings{bastide00miningminimal,
  address = {Heidelberg},
  author = {Bastide, Y. and Pasquier, N. and Taouil, R. and Stumme, G. and Lakhal, L.},
  booktitle = {Computational Logic --- CL 2000 Proc. CL'00},
  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.},
  interhash = {dc10d0ad3c40463f049ac775cb250f3d},
  intrahash = {25fa3431e7bdd7057f9b80a7385cd718},
  page = {972-986},
  publisher = {Springer},
  series = {LNAI},
  title = {Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/DOOD00.pdf},
  volume = 1861,
  year = 2000
}

@article{bastide00miningfrequent,
  author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.},
  interhash = {86c9586115ef1c7ec8539257849d9842},
  intrahash = {1e79f8ba76044d2c78cea441eeba84aa},
  journal = {SIGKDD Explorations, Special Issue on Scalable Algorithms},
  number = 2,
  pages = {71-80},
  title = {Mining Frequent Patterns with Counting Inference.},
  volume = 2,
  year = 2000
}