@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
}

@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{ivncsy2006frequent,
  abstract = {Abstract: Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs.},
  author = {Iváncsy, Renáta and Vajk, István},
  interhash = {5612ed1c8203908fb94adf7ad8304e12},
  intrahash = {f29f4627c9ae99370fc7ba005982e2e6},
  journal = {Acta Polytechnica Hungarica},
  number = 1,
  title = {Frequent Pattern Mining in Web Log Data},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.4559},
  volume = 3,
  year = 2006
}

@article{tkde06,
  author = {Lucchese, Claudio and Orlando, Salvatore and Perego, Raffaele},
  interhash = {33820f389bc1f6bdb96f5a8f925df879},
  intrahash = {3aff1098bf9828a0c6683f07145d60bb},
  journal = {IEEE Transactions On Knowledge and Data Engineering},
  number = 1,
  pages = {21--36},
  title = {Fast and Memory Efficient Mining of Frequent Closed Itemsets},
  volume = 18,
  year = 2006
}

@article{DT99,
  author = {Dehaspe, L. and Toivonen, H.},
  interhash = {eb17bc1d3cea0dc35d4875bd5386a5d7},
  intrahash = {53fa388ae74f5dec8c8fb89b66006fed},
  journal = {Data Mining and Knowledge Discovery},
  location = {Santa Barbara, CA},
  number = 1,
  pages = {7--36},
  title = {Discovery of Frequent Datalog Patterns},
  volume = 3,
  year = 1999
}

@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{orlando02efficient,
  abstract = {Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets
are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.},
  author = {Orlando, Salvatore and Palmerini, Paolo and Perego, Raffaele and Silvestri, Fabrizio},
  booktitle = {High Performance Computing for Computational Science — VECPAR 2002},
  interhash = {50c17d100341c01892f7dd8fbd7deb69},
  intrahash = {522c68b8bb5e28f1bf9f1e11e612f542},
  pages = {3--29},
  title = {An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets},
  url = {http://dx.doi.org/10.1007/3-540-36569-9_28},
  year = 2003
}