@article{agrawal1993mining,
  acmid = {170072},
  address = {New York, NY, USA},
  author = {Agrawal, Rakesh and Imieli\'{n}ski, Tomasz and Swami, Arun},
  doi = {10.1145/170036.170072},
  interhash = {53341ce3e6ce51c3bcf8b0219ec239b5},
  intrahash = {8730417fb8d6eff31b43254b67d09f83},
  issn = {0163-5808},
  issue_date = {June 1, 1993},
  journal = {SIGMOD Rec.},
  month = jun,
  number = 2,
  numpages = {10},
  pages = {207--216},
  publisher = {ACM},
  title = {Mining Association Rules Between Sets of Items in Large Databases},
  url = {http://doi.acm.org/10.1145/170036.170072},
  volume = 22,
  year = 1993
}

@article{pasquier1999efficient,
  abstract = {Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data. },
  author = {Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi},
  doi = {http://dx.doi.org/10.1016/S0306-4379(99)00003-4},
  interhash = {14f55460561c18e8d47a1ffaad6bb738},
  intrahash = {dd4cac14856b487b0819ebe042301d56},
  issn = {0306-4379},
  journal = {Information Systems },
  number = 1,
  pages = {25 - 46},
  title = {Efficient mining of association rules using closed itemset lattices },
  url = {http://www.sciencedirect.com/science/article/pii/S0306437999000034},
  volume = 24,
  year = 1999
}

@incollection{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 },
  author = {Lakhal, Lotfi and Stumme, Gerd},
  booktitle = {Formal Concept Analysis},
  doi = {10.1007/11528784_10},
  editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf},
  interhash = {f5777a0f9dccfcf4f9968119d77297fc},
  intrahash = {fbb41ddbb0a52d1ecca438655e652f09},
  isbn = {978-3-540-27891-7},
  language = {English},
  pages = {180-195},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Efficient Mining of Association Rules Based on Formal Concept Analysis},
  url = {http://dx.doi.org/10.1007/11528784_10},
  volume = 3626,
  year = 2005
}

@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
}

@incollection{citeulike:1377860,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  author = {Schmitz, Christoph and Hotho, Andreas and J\"{a}schke, Robert and Stumme, Gerd},
  citeulike-article-id = {1377860},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-34416-0\_28},
  doi = {10.1007/3-540-34416-0\_28},
  interhash = {b4a63aa5632ff093c2d345005fa16a17},
  intrahash = {06ea55e8751a06c3b44a92543dd6e85a},
  journal = {Data Science and Classification},
  pages = {261--270},
  posted-at = {2008-04-27 16:27:04},
  priority = {5},
  title = {Mining Association Rules in Folksonomies},
  url = {http://dx.doi.org/10.1007/3-540-34416-0\_28},
  year = 2006
}